DEV Community: Barbara Morara

Regression and Regularization: A Beginner’s Guide to Better Predictive Models

Barbara Morara — Fri, 24 Jul 2026 18:18:55 +0000

Introduction

Sometimes, in data science, there is a need to predict the future results based on some historical data. The regression is a machine learning method used for finding relations between variables and making predictions on the numeric values like prices, sales or demands.

But complex models are prone to the effect of overfitting, which means that they study the training data too accurately and have poor results on the testing ones. That is why regularization is necessary to avoid this problem and make models simpler and better-generalized.

What Is Regression?

The regression is the supervised machine learning method which predicts the numeric value depending on one or several input variables.

For instance, the real estate company may use regression to find the price of houses according to:

Size of the house.
Number of bedrooms.
Location.
Age of the house.

Types of Regression

Linear Regression
Linear regression is the simplest regression method which supposes linear dependence between the input variables and target value.

Example:

from sklearn.linear_model import LinearRegression

model = LinearRegression()
model.fit(X_train, y_train)

Multiple Linear Regression
The regression method which uses several predictors for its estimations, for instance, predicting the salary according to the education level, experience and job role of the employee.

Polynomial Regression
The regression method used in cases where the dependencies between the variables are more complicated and do not depend on the straight line.

The Problem of Overfitting

If the machine learning model has extremely good performance on training data, but poor performance on testing ones – then it probably suffers from overfitting.
It means that the model studied some noises or unnecessary dependencies while studying training data.
To solve this problem, regularization is needed.

What is Regularization?

Regularization is a method where a penalty term is added to the regression model to avoid over-fitting or complexity in the model. It makes sure that the model doesn't become dependent on specific features.

Types of Regularization are:

Ridge Regression (L2)
Ridge shrinks the coefficients without excluding the features.

from sklearn.linear_model import Ridge

model = Ridge(alpha=1.0)

** Lasso Regression (L1)**
Lasso shrinks some of the coefficients to make them zero, which means some features would be excluded.

from sklearn.linear_model import Lasso

model = Lasso(alpha=0.1)

Elastic Net

It combines Ridge and Lasso together to get both the benefits of both.

Regression	Regularization
Used for prediction and understanding relationships	Used to improve model performance
Minimizes prediction errors	Reduces model complexity
Can overfit with many features	Helps prevent overfitting

Regression is one of the most commonly used models in data science to predict numerical results. There are two important concepts of regression, namely regularization and regression.
Regularization helps in creating simpler models that are more robust in the case of real-world applications.

Machine Learning Explained: Understanding the Different Types of Machine Learning

Barbara Morara — Fri, 24 Jul 2026 17:57:55 +0000

Introduction

Currently, our everyday lives generate a lot of data. From recommending products while online shopping and predicting which posts will be in your social media feed, through detecting fraud and medical diagnosis – all of these require algorithms that can learn from data and make decisions. Machine Learning (ML) is here for that.

Machine Learning is a branch of Artificial Intelligence (AI), which is aimed at teaching machines to learn from data, identify patterns, and make decisions with little or no human involvement. It differs from traditional software programs as they follow certain instructions, while machine learning algorithms analyze data samples, learn relationships between them, and draw conclusions.

From the perspective of a data scientist, ML is one of the key techniques to extract insights from the available data and apply it to automation.

What is Machine Learning?

Machine Learning is an algorithmic process that includes learning patterns in the historical data and applying them to predictions or tasks execution.

Traditionally, program is created according to this pattern:

Rules + Data → Output
The programmer writes a set of rules to calculate taxes depending on income.

The machine learning program works differently:

Data + Expected Outputs → Learning Algorithm → Model

The computer discovers rules by examining examples.

Instead of writing a program that will distinguish between spam email and not spam, we feed the learning algorithm thousands of examples, and the model learns to detect spam.

Why Machine Learning is Important?

Machine Learning has become a must in the world of data and advanced computing capabilities. Organizations implement ML in order to solve problems.

A few common applications of machine learning are:

1. Recommendation Systems
Recommendation systems on sites such as Netflix, YouTube, and other online stores are developed using machine learning algorithms which suggest content or products based on user activities.

2. Healthcare
Machine learning algorithms are useful for disease detection, analyzing medical images, and predicting risk for patients.

3. Finance
Banks utilize machine learning for fraud detection, credit scoring, and market analysis.

4. Transportation
Autonomous vehicles use machine learning algorithms to understand surroundings and take appropriate driving actions.

5. Marketing
Companies analyze customer behavior to design customized advertisements.

Machine Learning Types
Machine learning can be broadly classified into four major categories:

Supervised learning
Unsupervised learning
Semi-supervised learning
Reinforcement learning These types differ based on how they learn from the data and the problem they solve.

1. Supervised learning
Supervised learning is the most popular form of machine learning. In supervised learning models, learning occurs based on data where the output has been provided. The input data already contains the answer or label.

The objective is to learn the relationship between input variables and output variables in order to be able to predict future instances of data.

Example:
Suppose we wish to predict the prices of houses.

The dataset may contain:

Size of House	Number of Rooms	Location	Price
1200 sq ft	3	Nairobi	8 million
2000 sq ft	5	Nairobi	15 million

The algorithm will learn about the relation of size and location to prices so that we may predict prices of a new house.

Types of Supervised Learning:

a) Regression
Regression involves predicting continuous numeric values.

Example:

Predicting house prices.
Sales forecasting.
Temperature prediction.
Forecasting future stock prices.

Common regression algorithms are:

Linear Regression
Decision Tree Regression
Random Forest Regression
Support Vector Regression

Example

from sklearn.linear_model import LinearRegression

model = LinearRegression()

model.fit(X_train, y_train)

prediction = model.predict(X_test)

b) Classification

Classification predicts classes or categories.

Examples:

Email: Spam or Not Spam
Disease prediction: Positive or Negative
Client: Will purchase or Will not purchase

Examples of classification algorithms:

Logistic Regression
Decision Trees
Random Forest
Support Vector Machines
Neural Networks

Example:

A bank might use classification to check if the transaction is fraudulent.

2. Unsupervised Learning
In contrast with the supervised learning, the data used in the unsupervised learning is unlabeled. In other words, the machine learning algorithm gets data and there are no answers given to the algorithm.
The aim of the unsupervised learning is to find out natural structure in data.

Example:

A firm has client data but it does not know client clusters. Machine learning algorithm can automatically discover the clusters according to their purchasing behavior.

Types of Unsupervised Learning

a) Clustering
Clustering groups together similar data.

Examples:

Customer segmentation
Similar documents grouping
Communities detection in networks

Clustering algorithms:

K-Means Clustering
Hierarchical Clustering
DBSCAN

Example:

A supermarket could group customers into:

Frequent buyers
Occasional buyers
Important clients

b) Dimensionality Reduction
Dimensionality reduction reduces the number of variables while keeping important information.
Sometimes large dataset includes hundreds of features and it becomes impossible to analyze data. Therefore, it is necessary to simplify data.

Popular dimensionality reduction techniques:

Principal Component Analysis (PCA)
t-SNE

Applications of dimensionality reduction:

Data visualization
Faster model training
Unnecessary features removal

3. Semi-Supervised Learning
Semi-supervised learning is a combination of both supervised and unsupervised learning.

It uses:

Labeled data (a little bit)
Unlabeled data (a lot)

Labelling data can be expensive and time consuming. Semi-supervised learning is useful when the number of labeled data is limited.

For instance,

A firm needs to develop an image recognition system. Rather than labeling all million images manually, it would label some of the images and let the model learn on its own from the unlabeled images.

Applications include:

Image Classification
Speech Recognition
Medical data analysis

4. Reinforcement Learning
Reinforcement learning is grounded on learning from interaction with the environment.

The agent learns through:

Performing actions
Getting rewarded or penalized
Improving future decisions

The objective is to increase the rewards in the future.

For instance,

A robot learning

How to move around the room
Getting the rewards for moving right
Being punished for hitting obstacles

In the long run, it learns the optimal path

Applications of Reinforcement Learning

Gaming
AI agents use reinforcement learning to master gaming and come up with effective strategies.

Robotics
Robots learn how to perform certain operations such as walking, grasping objects, etc.

Autonomous Vehicles
Vehicles learn driving behavior based on various situations.
Machine Learning Workflow

Machine learning projects usually consist of the following steps:

1. Data Collection
Collection of the relevant data from the database, website, sensors, survey, etc.

2. Data Cleaning
Eliminating errors and missing values from the dataset.

3. Feature Selection
Selecting relevant variables that will assist in predictions.

4. Model Training
Learning of patterns in training dataset by the algorithm.

5. Model Evaluation
Evaluation of the model through accuracy, precision, recall, Mean Squared Error, etc.

6. Model Deployment
Deployment of the learned model into production

Challenges in Machine Learning

1. Poor Data
Low-quality data generates low-quality predictions.

The popular phrase in the data science industry goes as follows:

"Garbage in, garbage out."

2. Overfitting
Overfitting takes place when a machine learning model trains too closely to the training data and does not function properly with other data.

3. Bias
Bias in the data causes bias in the results generated by machine learning algorithms.

4. Computing Requirements

Large-scale machine learning models require lots of computing power.

Differences between AI and Machine Learning

While these terms are interchangeable, they refer to different things.

Artificial Intelligence is the general idea of creating machines capable of performing the tasks that require human intellect.

Machine Learning is a subfield of Artificial Intelligence concerned with providing such machines with the ability to learn from the data.

Relationship is as follows:

Artificial Intelligence -> Machine Learning -> Deep Learning

Deep Learning is a field within machine learning where the artificial neural networks are used to solve challenging problems.

Conclusion

Machine Learning has completely revolutionized data analysis, automation, and decision making processes in modern businesses. Thanks to machine learning, computers can be trained on experience.
Major machine learning types such as supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning give various ways to solve specific problems based on the data type and required outcome.

For everyone stepping into the field of data science, knowing about the machine learning is crucial as these concepts represent the core of model and data-driven solution creation.

Parametric vs Non-Parametric Statistical Tests

Barbara Morara — Fri, 24 Jul 2026 16:59:25 +0000

In statistics, statistical tests refer to specific procedures and techniques for data analysis aimed at decision-making, pattern recognition, and determining whether observed effects are significant and happened not accidentally. There are two main classes of statistical tests: parametric and non-parametric tests. To understand their distinctions and to pick up the right one in specific cases, it is vital to learn the peculiarities of each type.

Parametric Tests

First of all, what is the definition of parametric tests? This is a class of tests that assumes that the data distribution is known. In many cases, it is assumed to be normally distributed. Besides, parametric tests are based on the assumption that some parameters can describe the analyzed population.

Some examples of parametric tests are:

T-tests, which allow comparing means of two groups;
ANOVA (analysis of variance), which enables comparison of means of three and more groups;
Pearson correlation test, which measures the correlation between two continuous variables.

Thus, for instance, a data scientist uses a t-test to define the difference in average income between two groups of customers.

The good thing about parametric tests is that their results are more precise when all assumptions are met. Moreover, they have higher statistical power than other tests, and, therefore, they are more effective when detecting differences.

At the same time, there are some requirements for data:

It must be approximately normally distributed;
Observations must be independent;
Some cases require the similarity of variances between groups.

What Are Non-Parametric Tests?

Non-parametric tests are statistical tools that do not require strict requirements regarding the data distribution. In other words, these tests are known as distribution-free tests because they are applicable even when the data is not distributed normally.

The most popular examples of non-parametric tests are:

Mann-Whitney U test - alternative for the independent t-test.
Wilcoxon signed-rank test - for comparisons of paired observations.
Kruskal-Wallis test - alternative for ANOVA.
Spearman's rank correlation test - used to measure relationships between ranked variables.

For instance, when a data scientist uses customer satisfaction ratings gathered using the rating from 1 to 5, such data is not likely to fit the requirements for a parametric test.

Using non-parametric tests allows handling:

Small samples.
Ordinal data (ranked data).
Data containing outliers.
Non-normal distributions.

However, the main drawback of these tests is the lower power compared to the parametric approach.

Feature	Parametric Tests	Non-Parametric Tests
Data assumptions	Require assumptions about distribution	Few or no distribution assumptions
Data type	Mainly continuous numerical data	Can handle ordinal and non-normal data
Main measurement	Uses means and variances	Often uses ranks or medians
Statistical power	Higher when assumptions are met	Lower but more flexible
Examples	T-test, ANOVA, Pearson correlation	Mann–Whitney, Kruskal–Wallis, Spearman correlation

When Should Data Scientists Use Each Approach?

Choosing between parametric and non-parametric tests depends on the features of the dataset.

Choose Parametric Tests in Cases Where:

The dataset is sufficiently large and normally distributed.
Variables are measurements.
Test assumptions can be checked.
More powerful statistical analyses are needed.

For example, studying the average test scores of the students of a large population is the right scenario to use a t-test.

Choose Non-Parametric Tests in Cases Where:

The data is not normally distributed.
The sample size is low.
The dataset includes outliers.
Data is categorical or ranked.

For example, analyzing customer reviews or satisfaction rankings may require non-parametric methods because the data is ordinal rather than continuous.

The Role of These Tests in Data Science

Both parametric and non-parametric tests play an important role in modern data science. Before building machine learning models, data scientists often perform statistical tests to understand relationships between variables, evaluate hypotheses, and identify important features.

Parametric tests are commonly used in predictive modeling, experimental analysis, and A/B testing because they provide efficient estimates when assumptions are met. For example, companies may use statistical tests to determine whether a new website design improves customer conversion rates.

Non-parametric tests are valuable in real-world datasets where data is often messy, incomplete, or does not follow ideal distributions. They allow data scientists to analyze information without forcing unrealistic assumptions.

Ultimately, the choice between parametric and non-parametric tests depends on understanding the data. A good data scientist does not automatically choose one method but evaluates the dataset, checks assumptions, and selects the statistical approach that provides the most reliable insights.

Conclusion

Parametric and non-parametric tests are both important tools in statistical analysis. Parametric tests offer powerful analysis when data meets specific assumptions, while non-parametric tests provide flexibility when those assumptions are not satisfied. In data science, knowing when to apply each method helps professionals make accurate conclusions, improve models, and make better data-driven decisions.

Why Hypothesis Testing Matters in Data Science: Understanding the Tests Every Data Scientist Should Know

Barbara Morara — Fri, 24 Jul 2026 16:38:22 +0000

Data science encompasses more than model creation and visualization. A significant portion of data science consists of evidence-based decision-making. Often when dealing with data, we have to answer such questions as:

Did the newly introduced marketing strategy contribute to sales growth?
Is there any advantage in applying a novel treatment method?
Are there any significant differences between groups?
Is the pattern that we observe true or happened by mere coincidence?

It is where hypothesis testing comes into play.
Hypothesis testing is a statistical procedure aimed at determining if there is sufficient evidence in a sample to back up a claim or refute an assumption.
In order to reach conclusions based on data, hypothesis testing allows going beyond mere assumptions and observations.

What Is Hypothesis Testing?

Hypothesis testing involves the following two claims:

Null Hypothesis (H₀)
The null hypothesis represents an assumption that there is no significant difference or relationship.

Examples:

The newly applied teaching approach does not influence student performance.
A product's price does not impact its popularity.

Alternative Hypothesis (H₁)
The alternative hypothesis stands for what we want to prove.

Examples:

The newly applied teaching approach influences student performance.
A product's price impacts its popularity.

The purpose of hypothesis testing is to analyze data and find out if there is sufficient evidence to reject the null hypothesis.

Why Hypothesis Testing Is Important in Data Science

1. Helps with Evidence-Based Decision Making
Data scientists usually face incomplete data. Hypothesis testing helps identify which patterns in data are meaningful.

For instance:
A firm introduces a new design of the website and finds out that it is followed by sales growth.

Now we have a question:
Was the growth caused by introducing the new design, or did it happen randomly?

A hypothesis test will tell whether the improvement is statistically significant.

2. Useful in Validation of Machine Learning Features
Before developing machine learning models, data scientists try to understand relationships between variables.

For instance:
A data scientist wants to forecast house prices.
He/she may ask:

Does the location significantly impact the house price?
Does the house size influence the price?
Does the number of rooms influence the price?

Hypothesis tests help to find out which features should be included in the models.

Commonly Used Hypothesis Tests in Data Science

1. Z-Test
Z-test is used when we need to compare the sample mean with the population mean, the population variance is known, and the sample size is large.

For instance:

A company says that the average delivery time is 30 minutes.
A data scientist obtains data about delivery time from 100 customers.
The question:

Is the real delivery time different from 30 minutes? Z-test will tell us whether the company's statement is correct.

Importance in Data Science:

Tests business statements
Comparison of big datasets
Quality control analysis

2. T-Test
T-test is used when we need to compare the means of two groups to find whether the difference between them is statistically significant.

Different types:

One-sample t-test
Independent two-sample t-test
Paired t-test

For instance:
A company wants to know whether employees trained perform better than those who were not trained.

Group A:
Untrained employees
Group B:
Trained employees

T-test will show whether there is a significant difference in their performance.

Importance in Data Science:
T-tests are used in:

A/B testing
Customer group comparison
Experiments analysis

For instance:
Streaming company tests two recommendation systems.

Version A → Old system
Version B → New system

The t-test helps determine if the new system improves user engagement.

3. Chi-Square Test
The Chi-square test is applied to test an association between categorical variables.

Example:
A company wants to check whether the gender of customers influences product preferences.

Data:

Gender	Product Choice
Male	Laptop
Female	Phone

The Chi-square test checks whether the preference for the products depends on gender.

Importance in Data Science:
Used for:

Feature selection
Customer behavior analysis
Survey data analysis
Category relationship testing

Example:
In machine learning, a data scientist could apply Chi-square tests for feature selection before the model training.

4. ANOVA (Analysis of Variance)
ANOVA is used when we need to compare the means of three or more groups.
A t-test compares only two groups while ANOVA allows us to make many comparisons.

Example:
A company wants to test three advertising approaches:

Facebook ads
Google ads
Instagram ads

The question:

Which approach generates higher sales? ANOVA will tell us whether the differences between groups are statistically significant.

Importance in Data Science:
Used in:

Experiment analysis
Market research
Product testing
Scientific research

5. Correlation Tests
Correlation tests check whether two variables are related.
Popular tests are:

Pearson correlation
Spearman correlation

Example:

A data scientist would like to test whether there is any relation between study hours and exam scores.

Correlation test measures the relation between the increase of study hours and score increase.

Importance in Data Science:
Used for:

Exploratory data analysis
Feature selection
Variables relationship detection However, correlation doesn't imply causation.

Example:
There can be a positive correlation between ice cream sales and number of drownings because both variables grow during summer.

6. Tests for Normal Distribution
Before carrying out some statistical tests, it is important to know whether your data fits a normal distribution.

Normality tests are:

Shapiro-Wilk test
Kolmogorov-Smirnov test

Example:

A data scientist would like to analyze the spending patterns of customers.

Before conducting a t-test, he/she will determine:

Is the distribution of the customer spending normal?
If not, a non-parametric test will be carr ied out.

Importance of this Test in Data Science:
Helps in:

Determining the appropriate tests to apply
Understanding the behavior of the data
Making better assumptions

7. Non-Parametric Tests
Non-parametric tests are conducted when certain assumptions required for traditional tests do not hold.

Examples:
Mann-Whitney U test
Alternative to the independent t-test.

When you compare two groups without assuming a normal distribution.

Example:

Comparing the customer satisfaction between two companies.

Wilcoxon Signed-rank test

Alternative to a paired t-test.

Example:

Comparing the customer satisfaction levels before and after service improvement.

Kruskal-Wallis test

Alternative to ANOVA.

When you compare three or more groups.

Example:

Comparing salaries in different industries.

8. Runs Test

Runs test determines if the data is randomly distributed.

Example:

An analyst would like to know if there is any pattern in stock prices.
He/She uses the runs test to determine the randomness of data.

Importance of this Test in Data Science:

Applied in:

Time series analysis
Financial modeling
Quality control

9. Kolmogorov-Smirnov (K-S) Test
This test is used to compare the distribution of the data to that of the theoretical distribution.

Example:

A data scientist would like to find out:

Is this dataset normally distributed?

The K-S test measures the difference between the observed and the theoretical distribution.

Importance:
Applied in:

Data preprocessing
Model assumptions
Dataset comparison

Examples of Hypothesis Testing in Real Data Science Projects
Let's imagine that a company plans to introduce a new mobile application.

A data scientist can apply:

T-test
To test user engagement before and after the app update.

Chi-square test
To analyze whether demographic variables influence app usage.

ANOVA
To compare the performance of different marketing campaigns.

Correlation analysis
To reveal the factors affecting customer retention.

Normality tests
To choose the methods for statistical analysis.

All these tests provide proof for making more informed decisions.

Limitations of Hypothesis Testing

Despite the power of hypothesis testing, there are some limitations.

1. Statistical significance doesn't mean practical significance
The finding can be statistically significant but have no practical value.

For example:
The loading time of the website is reduced by 0.2 seconds after an update. The difference can be statistically significant but irrelevant for users.

2. Big sample sizes can increase the chances of getting statistically significant results
With millions of observations, even slight differences can become significant.

3. Results depend on certain assumptions
Some tests make assumptions about:

Sample size
Distribution
Independence of observations

Conclusion
Hypothesis testing is one of the cornerstones of data science as it allows us to base our decisions on proof and not just assumptions.
T-test, Chi-square test, ANOVA, normality test, and non-parametric tests serve different purposes.
A good data scientist is not just able to create models but also understand the statistical background of the data.
By knowing how to conduct hypothesis testing, data scientists can answer questions and prove their findings.

Understanding Data Distributions and Their Impact on Data Science

Barbara Morara — Fri, 24 Jul 2026 15:30:59 +0000

When you first start learning data science, there are some areas like code, machine learning algorithms, and visualization tools that might draw your attention. However, one thing to note from all of this is data distribution.

Knowing about the data distribution will help you make informed decisions while cleaning data, visualizing, performing statistical analysis, and training the machine learning algorithm. Prior to training a machine learning algorithm, it is advisable to get acquainted with data distribution.

In this article, we will be discussing what data distributions are, why are they important, and also how we can visualize them using examples in Python.

What It A Data Distribution?

It is a way through which data in a dataset is described in terms of:

Where the values cluster
Is it uniformly distributed or not?
Outliers in the data
Symmetry in the distribution or skewness in it

Imagine a class room setting where students write an exam in mathematics. If most of the students have scored between 70 and 80 marks, then the distribution is clustered around these values. Also, if a few students have extremely low or high scores, then the distribution changes.
Getting this insight is crucial for data scientists before analyzing the dataset.

Importance of Data Distribution

It is crucial to know data distribution as it will help:

Pick appropriate statistical methods
Identify outliers
Manage missing values
Figure out if data should be transformed
Use the correct machine learning algorithms
Boost model performance

Lack of knowledge about data distribution leads to incorrect results and predictions.

Types of Data Distributions

1. Normal Distribution
Normal distribution is among the most common distributions in statistics.
It is characterized by a bell-shaped graph where:

Values tend to cluster around the center.
Mean, median, and mode are approximately equal.
Frequencies decline while moving away from the center.

Some examples include:

Height of people
IQ score distribution
Random measurement error

2. Skewed Distribution
Skewed distribution does not have symmetry.
There are two subtypes:

Right-Skewed (Positive Skew)
The majority of values are low while there are a few high values.
Examples:

Income
Housing prices
Online spending

Left-Skewed (Negative Skew)
Values are mostly high while there are few low values.
Examples:

Exam grade distribution after an easy test
Consumer satisfaction

3. Uniform Distribution
A uniform distribution is where all values have the same probability.
Example:

Rolling a dice.

Any number from 1 to 6 is equally likely to show up.

4. Bimodal Distribution
Bimodal distribution has two peaks.
It usually happens in case data is drawn from two populations.
Example:

Height distribution of male and female students.

Measuring Distribution with Statistics

Besides visualization, we can summarize distributions using statistical measures.

Mean
The average value.

import numpy as np

data = [12, 15, 18, 20, 25]

print(np.mean(data))

Median
The middle value after sorting the data.

import numpy as np

data = [12, 15, 18, 20, 25]

print(np.median(data))

Median comes in handy in the case of outliers since it is not sensitive to extreme data points.

Standard Deviation

Standard deviation shows how much the data is spread out.

import numpy as np

data = [12, 15, 18, 20, 25]

print(np.std(data))

Small standard deviation – data points are closely packed together.
Large standard deviation – data points are more spread out from the mean.

Skewness Detection

In Python, Pandas provides an easy way to calculate skewness.

import pandas as pd

data = pd.Series([2, 3, 4, 5, 6, 20])

print(data.skew())

Positive value → Right-skewed
Negative value → Left-skewed
Near zero → Approximately normal

Why Data Distribution Is Important for Machine Learning?

Most of the machine learning models work with data which is distributed in a particular way.

For instance:

Linear regression works better with approximately normally distributed variables.
Logistic regression is positively influenced by good behavior of input features.
Naive bayes assumes probability distribution in many cases.
Some clustering and anomaly detection methods depend on distribution assumptions.

When your data is highly skewed, model accuracy will likely decrease.

Non-Normal Data Handling Techniques

What should be done when your data doesn't follow a normal distribution? Don't panic – here are several options:

Remove or investigate potential outliers.
Apply transformation – logarithmic, square root, etc.
Normalize or scale the data.
Use algorithms which are less sensitive to distribution of data.

The most effective depends on your data and problem to solve.

Best Practices

Before implementing a machine learning algorithm, do the following things:

Investigate your data using histograms or box plots.
Detect skewness and outliers.
Calculate summary statistics.
Understand whether your data fulfills the requirements of your model.
Apply transformations if required

It is crucial to have an understanding of how data is distributed for any data scientist. The knowledge allows you to understand better your data set, apply proper statistical methods, and develop machine learning algorithms effectively.

Always take some time to explore your data before starting with training models. In five minutes, you may get valuable information about your dataset that could remain hidden from you otherwise. "Knowing your data before making your model know it."

Be it customer analytics, sales forecast, or AI solutions development; understanding data distribution would help you to make better decisions.

Python and Its Role in Data Analytics

Barbara Morara — Mon, 25 May 2026 14:20:31 +0000

Introduction

Technology has affected the way that both people and organizations make their decisions today. It is common practice for corporations, governmental bodies, schools, hospitals, and even small businesses to use information in understanding the problem and offering an improved service. The process of collecting, cleansing, analyzing, and interpreting data is called data analytics. One of the programming languages that are widely used in this context is Python.

Python has become one of the essential technologies in the modern world since it is easy to learn, versatile, and highly efficient. Beginners have no difficulties mastering the syntax, while professionals use this language for machine learning, artificial intelligence, automation, and big data analysis. In data analytics, Python is helpful in performing such activities as cleaning raw data, carrying out calculations, generating charts, and making predictions.

This article provides a brief explanation of what Python is, why it has gained popularity among data analysts, which libraries are employed for analytics, how Python is used in practice, and what benefits can beginners derive from studying this language.

What Is Python?

It is a high-level language developed by Guido van Rossum and launched in the year 1991. The language was developed in such a way that it would be easy to learn. As compared to other programming languages that have difficult syntaxes, Python has straightforward commands similar to English.

For example, printing a message in Python only requires:

print("Hello World")

Such an easy and flexible language is ideal for beginners learning programming.

Moreover, Python is an open-source programming language. This means that people can get access to it and install it without paying any fee. It runs on several operating systems such as Windows, Linux, and Mac OS. The versatility of Python has enabled it to be applied in the following fields:

Web development
Data analytics
Artificial intelligence
Machine learning
Cybersecurity
Automation
Scientific computing
Software development

From among the above applications, it is worth noting that one area that has seen Python gain immense popularity is data analytics.

Understanding Data Analytics

Before getting into the use of Python in analytics, it is necessary to know the concept of data analytics.

Data analytics can be understood as the activity of analyzing data in order to uncover some patterns, trends, and useful information. Various organizations have a lot of data available every day. For instance:

Banks get customer transaction data.
Hospitals maintain patient data.
Schools have data about student performance.
Social media maintains data related to interactions between users.
Retail stores maintain sales data.

Raw data itself is not valuable unless analyzed properly through data analytics. Some of the questions that organizations ask by using data analytics include:

Which products are sold the most?
What leads to customer complaints?
Which marketing method should be followed?
How should costs be minimized?
What will be the future trends?

These tasks can be done effectively using Python.

Why Python Is Popular in Data Analytics

1. Easy to Learn and Use
Firstly, another important reason for the popularity of Python is its simplicity. Python language offers clear syntax that is easy to learn. Even beginners can easily learn Python programming without much knowledge of computer science.

Python is a language that does not require many lines of codes to perform tasks. This helps save time and simplifies things.

For example:

numbers = [1, 2, 3, 4, 5]
print(sum(numbers))

The code above calculates the sum of numbers in a simple way.

2. Large Collection of Libraries

A lot of libraries exist in Python that make data analysis simpler. A library can be described as pre-written code by programmers which enables other programmers to perform tasks faster.

Some well-known libraries for data analytics in Python include:

Pandas
NumPy
Matplotlib
Seaborn
Scikit-learn

3. Big Community Support

There is a big global community of Python developers and data analysts. That means when you start to learn this programming language, you can find many tutorials, videos, forums, and documents online.

If you experience any issues while writing your code, there is a high probability that someone else has already found a solution and posted it online.

4. Integrations With Other Technologies

It allows integration with databases, APIs, cloud platforms, and web services. That makes Python useful for modern systems for working with data.

For instance, Python can be connected to:

MySQL databases
Excel spreadsheets
JSON APIs
web applications
Machine learning systems

5. Perfect Visualization Capabilities

It is important to visualize your data because charts and graphs allow you to interpret information quickly. Python offers perfect visualization capabilities which allow you to generate:

Bar charts
Pie charts
Histograms
Scatter plots
Heat maps
Dashboards

Python Libraries Used in Data Analytics

1.Pandas
Pandas library is among the most significant libraries in Python for data analysis. It facilitates easy data analysis using tabular data known as DataFrames.

With Pandas, analysts can:

Read CSV and JSON files
Clean data
Remove duplicates
Filter rows
Sort values
Calculate statistics

Example:

import pandas as pd

data = pd.read_csv("sales.csv")
print(data.head())

This code reads a CSV file and displays the first rows.

2. NumPy

NumPy is a library that deals with numerics and mathematics. It handles arrays and matrices.

Functions of NumPy by analysts include:

Mathematical computations
Statistical computations
Analysis of large datasets

Example:

import numpy as np

numbers = np.array([10, 20, 30])
print(numbers.mean())

3. Matplotlib

Matplotlib is used for creating charts and graphs.

Example:

import matplotlib.pyplot as plt

x = [1,2,3]
y = [4,5,6]

plt.plot(x,y)
plt.show()

This creates a simple line graph.

4. Seaborn

Seaborn is built on top of Matplotlib and produces appealing statistical graphics.

Some common uses include:

Heat maps
Correlation graphs
Distribution graphics

5. Scikit-Learn

Scikit-Learn is used for machine learning and predictive analysis.

It enables analysts to:

Construct predictive models
Categorize data
Identify patterns
Conduct regression analysis

Uses of Python in Data Analytics

1. Data Collection

The first step in any data analytics process is collecting data. In Python, we can get data from the following sources:

Websites
APIs
Database
CSV Files
Excel Spreadsheets
JSON Files

For instance, data is retrieved from APIs using the requests module.

import requests

response = requests.get("https://dummyjson.com/products")
data = response.json()

2. Data Cleaning

Raw data may have some errors or missing information including:

Missing data
Duplicate data
Inaccurate data
Formatting problems

Pandas in Python makes data cleaning easy.

Example

data.drop_duplicates()
data.fillna(0)

Data cleaning enhances accuracy in data analysis.

3. Data Analysis

Data analysis involves finding patterns from the cleaned data by analysts.

Python can compute:

Averages
Percentages
Correlation
Trends
Rates of growth

Example

data["sales"].mean()

4. Data Visualization

Visualization aids in clear communication of findings.

Charts that can be created using Python include:

Sales trend analysis over time
Demographics of customers
Product performance graphs

Example:

plt.bar(products, sales)

5. Predictive Analytics

Additionally, Python is used to predict possible outcomes through machine learning.

These include:

Prediction of customer behavior
Sales forecasts
Detection of fraud
Prediction of diseases The algorithms are trained using previous datasets and patterns.

Uses of Python in Data Analytics in the Real World

1. Healthcare

Hospitals use Python to analyze their patient records to enhance the delivery of health services.

These uses include:

Disease prediction
Analysis of medical images
Patient monitoring
Pharmaceutical research

For instance, disease trends can be identified from patient records.

2. Banking and Financial Services

Python is used by banks to:

Detect fraud
Transaction analysis
Risk prediction
Automate reporting tasks

Institutions use Python in decision making as well as security.

3. E-Commerce

E-commerce stores analyze their customers' behavior using Python.

These include:

Product recommendations
Sales analysis
Customer profiling
Stock management

Firms such as Amazon use the data for product recommendations.

4. Social Media

Social media firms conduct user engagement analysis.

Data analysis enables these companies to:

Understand user interests
Trends in different social media platforms
Advertising effects

5. Education

Educational institutions use the data to keep track of student progress.

Python helps educational institutions:

Find out poor performers
Enhance learning
Attendance analysis

Reasons Why Beginners Should Learn Python

1. Very High in Demand in The Job Market

Skills of using Python are in high demand globally. Most employers seek individuals familiar with Python and data analytics.

Career pathways that one can pursue include being:

Data Analyst
Data Scientist
Machine Learning Engineer
Business Analyst
Software Developer

2. Easier to Learn than Other Programming Languages
Python programming language is easy for beginners to learn.

3. Flexibility

Knowing Python makes an individual flexible in choosing different career paths since after gaining knowledge in analytics, one can explore into:

Artificial intelligence
Web Development
Cybersecurity
Automation

4. Rich Learning Materials Available
There is plenty of material to help one master Python programming, including:

Tutorial websites
YouTube videos
Online documentation
Online course materials

This material is readily available online for no cost.

5. Enhances Problem Solving Skills
Python encourages learners to be analytical in their problem-solving skills.

Challenges Associated with the Use of Python in Data Analytics

Despite its numerous benefits, Python still poses challenges when used in data analytics, including:

1. Python is Slower than Other Languages
Python is slow compared to other programming languages because Python uses interpretation instead of compilation.

2. Consumes Large Amount of Memory
Handling large data sets in Python requires huge storage space.

3. Complex to Work With when Handling Large Projects
While easy for beginners to work with, Python is relatively complex when handling large projects.

Future of Python in Data Analytics

The future for python programming language is bright as companies keep producing lots of data that require analytics tools.

Some factors that will keep making Python vital include:

Rapid advancement of artificial intelligence
Development of big data technology
Increased automation activities
High demand for machine learning solutions

Most universities currently incorporate Python in their curriculum.

Conclusion

Python is one of the most effective programming languages utilized in data analysis. It is not only a user-friendly and flexible tool but also has an impressive library base.

Python can help an analyst perform all steps associated with data management effectively and easily. With the use of various libraries like pandas, NumPy, and Matplotlib, data analysis becomes much more efficient.

The use of Python in healthcare, financial institutions, educational facilities, and other areas helps companies make better decisions on the basis of collected information. This language will remain crucial in the development of the field under changing technologies.

For beginners who are interested in technology, data analysis and problem solving, Python is a great choice.

Connecting Power BI to a SQL Database: A Complete Guide

Barbara Morara — Wed, 08 Apr 2026 19:36:46 +0000

Introduction

Microsoft Power BI is a software for performing business intelligence analysis and generating data visualizations for making decisions. The software can be used for building interactive reports and dashboards.

Modern enterprises use databases for storing their structured information. PostgreSQL is one of the popular SQL databases used to store data. SQL databases are necessary as they enable efficient storage and management of information.

An organization can use Power BI to connect to its database in order to:

Use real-time data
Undertake more complex analysis
Make dashboards automatically
Make strategic decisions Storing data is only half the task; Power BI will help you make useful decisions out of it.

Connecting Power BI with a PostgreSQL Database Running Locally

Connecting Power BI with a local PostgreSQL database is easy and can be achieved in just a few simple steps.

Step 1: Launch Power BI Desktop

Open Power BI Desktop from your PC.

Step 2: Select “Get Data”

Click on Get Data from the Home tab.

Step 3: Select PostgreSQL Database

Select PostgreSQL database from the list of data connectors.

Step 4: Connection Details

Here a pop-up window opens where you will fill in the following details:

Server: example- localhost
Database: enter the name of your database

Step 5: Connection Modes

You will be able to choose one of the two modes below:

Import (importing data into Power BI)
DirectQuery (querying data)

Step 6: Login Details

Enter your PostgreSQL:

Username
Password

Step 7: Load Data

Following successful login:
Navigator pop-up will open
Choose the required table(s)

Click “Load”

Your PostgreSQL data is now loaded into Power BI.

Connecting Power BI to a Cloud Database (Aiven PostgreSQL)

Cloud databases such as Aiven PostgreSQL have further steps involved.

Step 1: Get Connection Information from Aiven

Information to gather from your Aiven console include:

Host (server name)
Port (eg;5432)
Name of the database
Username
Password

Step 2: Download SSL Certificate

As Aiven insists on secure connections, it will be necessary to download the SSL certificate found in the console.

Step 3: Connect Power BI

In Power BI:

Go to Get Data > PostgreSQL database
Add:
Server: host:port
Database name

Step 4: Set up SSL

Make an encrypted connection
Supply the SSL certificate if needed

Importance of SSL

SSL certificates provide:

Encryption of data in transit
Prevention of interceptions
Authentication of clients and servers

Without SSL, sensitive business information might get intercepted while in transit.

Loading and Data Modeling in Power BI

Once your connection is set up, you can load tables such as:

Customers table
Products table
Sales table
Inventory table

After that, Power BI identifies the relationships, but you can define them manually.

Creating Relationships

The most common relationships include:

Customers table → Sales table (Customer ID)
Products table → Sales table (Product ID)
Products table → Inventory table (Product ID)

Concepts of Data Modeling

Data modeling helps organize tables to enable Power BI to analyze data accurately:

Primary keys uniquely identify each row
Foreign keys connect different tables
Establishing relationships ensures proper aggregation and filtering of data

For instance:

A sales report can properly aggregate revenue by customer
An inventory report can relate inventory information to sales

Data modeling enhances:

Performance
Accuracy
Usability of reports

Conclusion: The Importance of SQL for Power BI Analysts

SQL skills are crucial for all Power BI analysts. Although Power BI comes with robust visualization features, SQL allows users to effectively interact with data sources.

Through SQL, analysts will be able to:

Query for the datasets they need
Filter the data
Perform aggregations such as SUM, COUNT, AVG
Combine multiple tables
Generate clean datasets prior to visualization

Using SQL together with Power BI in practical applications helps generate faster, accurate, and scalable reports.

Connecting Power BI to SQL databases such as PostgreSQL, whether hosted locally or in the cloud, maximizes the capabilities of data analysis. Through this knowledge on how to connect, secure the connection using SSL, and model data, analysts can gain valuable insights from data.

Understanding Joins and Window Functions in SQL

Barbara Morara — Mon, 02 Mar 2026 19:44:09 +0000

Two of the most powerful SQL concepts a data person, like a data analyst or a backend developer, will use are "Joins" and "Window Functions." "Joins" are used to combine data from multiple tables, and "Window Functions" are used to perform complex calculations on related rows of data without aggregating the results.

In this article, I will explain what "Joins" and "Window Functions" are, along with examples, in a very simple way.

Part 1: SQL Join

In relational databases, data is often stored in multiple tables. For example, you might have:
• customers table
• orders table

Each table stores different information, but they are connected using a common key like customer_id.

What Is a Join?

A JOIN combines rows from two or more tables based on a related column.

Think of it like matching records from one Excel sheet to another using a common ID

Types of Joins

1. INNER JOIN

Returns only the matching records in both tables.
SELECT c.customer_id, c.name, o.order_id, o.order_date FROM customers c INNER JOIN orders o ON c.customer_id=o.customer_id;

This query returns customers who have placed orders.

2. LEFT JOIN

Returns all records from the left table and matching records from the right table. If there is no match, NULL values appear.

SELECT c.customer_id, c.name, o.order_id FROM customers c LEFT JOIN orders o ON c.customer_id = o.customer_id;

This shows all customers, even those who haven’t placed any orders.

3. RIGHT JOIN

Returns all records from the right table and matching ones from the left.

SELECT c.customer_id, c.name, o.order_id FROM customers c RIGHT JOIN orders o ON c.customer_id = o.customer_id;

4. FULL OUTER JOIN

Returns all records from both tables, whether they match or not.

SELECT c.customer_id, c.name, o.order_id FROM customers c FULL OUTER JOIN orders o ON c.customer_id = o.customer_id;

How I Understand Joins

Joins are about relationships.
If two tables share a key, a join lets you bring their data together into one meaningful result.

Without joins, working with relational databases would be extremely limited.

Part 2: Window Functions

Window functions are more advanced and extremely powerful.

Unlike GROUP BY, window functions do not collapse rows. Instead, they perform calculations across a set of rows related to the current row.

That “set of rows” is called a window.

Basic Syntax

FUNCTION_NAME() OVER ( PARTITION BY column ORDER BY column )

•PARTITION BY → splits data into groups
•ORDER BY → defines row order inside each group

Common Window Functions

1. ROW_NUMBER()

Assigns a unique number to each row.
SELECT customer_id, order_id, ROW_NUMBER() OVER ( PARTITION BY customer_id ORDER BY order_date DESC ) AS row_num FROM orders;

This ranks each customer’s orders from newest to oldest.

2. RANK()

Assigns ranking, but allows ties.

SELECT product_id, revenue, RANK() OVER ( ORDER BY revenue DESC ) AS revenue_rank FROM products;

3. SUM() as a Window Function
SELECT customer_id, order_date, amount, SUM(amount) OVER ( PARTITION BY customer_id ORDER BY order_date ) AS running_total FROM orders;

This calculates a running total per customer.

GROUP BY vs Window Functions

GROUP BY reduces rows.
Window functions keep all rows and add calculated columns.

Example using GROUP BY:
SELECT customer_id, SUM(amount) AS total_spent FROM orders GROUP BY customer_id;

Example using Window Function:
SELECT customer_id, amount, SUM(amount) OVER (PARTITION BY customer_id) AS total_spent FROM orders;

The second query keeps each order visible while still showing total spent.

Why Joins and Window Functions Matter

If you’re working in analytics (Power BI, Excel, SQL Server, PostgreSQL, etc.), these two skills are essential:
•Joins help you combine datasets.
•Window functions help you analyze patterns within those datasets.

In real-world analysis:
•You join sales with customers.
•You calculate rankings.
•You create running totals.
•You detect duplicates.
•You identify top-performing products.

These are daily tasks for data analysts and backend engineers.

Summary of Key Points

1.Joins combine data from multiple tables using related columns.
2.INNER JOIN returns matching records only.
3.LEFT JOIN keeps all records from the left table.
4.Window functions perform calculations without grouping rows.
5.PARTITION BY divides data into logical groups.
6.Window functions are powerful for ranking, running totals, and advanced analytics.

If you truly understand Joins and Window Functions, you’ve crossed an important milestone in SQL mastery.

Joins teach you how data connects.
Window functions teach you how data behaves.

Master both — and you unlock advanced analytics capabilities that tools like Power BI and Tableau rely on under the hood.

How Analysts Translate Messy Data, DAX, and Dashboards into Action Using Power BI

Barbara Morara — Mon, 09 Feb 2026 11:33:00 +0000

In today’s information-driven world, lack of information is not what hurts the organization; what hurts the organization is how it interprets the information it has. As discussed, raw data is incomplete, inconsistent, and scattered across many sources, and here is where Power BI analysts come in.

While it is a visualization tool, it is more than that. Used well, Power BI is a decision-making machine that turns chaotic data into insights that leaders can act upon. In a sense, this article is a presentation of how analysts can use data cleaning, data analysis expression, and data visualization to drive business decisions.

1. From Messy Data to Reliable Information

Reality of Raw Data:
In actual businesses, data is never clean:

Missing Values
Similar Records
Misdated Information
Inconsistent use of names (e.g. "Nairobi", "NRB","Nai")
Negative Prices/Unrealistic Discounts

If this data is analyzed without cleaning, decisions made from the analysis will be wrong.

How analysts utilize Power BI to remedy this problem

Analysts can leverage Power Query for:

Remove Duplicates and Errors
Handle missing values logically
Standardized Columns & Formats
Merge data from various sources (e.g., Excel, database, API) Business Impact: Clean data helps to promote trust. Managerial decisions are easier to make with accurate reports.

2. Converting Data to Meaning with DAX

What is DAX really used for?

DAX, or Data Analysis Expressions, is less about complex formula writing and more about the articulation of business logic.
Analysts use DAX to answer questions like the following:

What are total sales this month vs. last month?
Which are the growing and which are the declining products?
What is the average time of delivery?
Do discounts affect the profit?

Example of business logic with DAX

Instead of just showing the numbers of sales, analysts produce:

YTD Sales
Profit margins
Percentages of growth
Performance rankings

Business impacts: DAX changes raw numbers into KPIs, which are understood and used by the executives.

3. Dashboards that Tell a Story, Not Just Show Charts

Why Dashboards Matter

A good dashboard should answer questions at a glance:

What is going on?
Why is it happening?
What should we do next?

Analysts create dashboards with:

Clear KPIs (Sales, Profit, Growth
Filters (Date, Region, Product)
Trends and comparisons
Alerts for poor performance

Business Impact: Decision-makers save time and act faster, as dashboards are intuitive and focused

4. Translating Insights into Action

Power BI in real business decisions
Power BI insights lead to actions such as:

Increasing stock levels for high-performing products
Cutting down on discounts affecting profit
Determining the underperforming
Improving delivery timelines
Adjusting sales strategies

Example

A dashboard indicates:

Sales are strong, but profit is declining
DAX shows heavy discounting in one region

Action:Management adjusts pricing strategy to increase profitability.
**Business Impact: **Power BI helps organizations act, not just observe.

5. Why Power BI Analysts Are Valuable

Those working in power BI analytics occupy an intermediate position between data and decision-makers. The value they offer lies in:

Understanding Business Problems
Correctly clean and model the data
Writing Meaningful DAX Measures
Designing dashboards that communicate clearly

They don't simply build reports; they build better decisions.

Power BI is more than just a technical tool. When used well, it can become a bridge between messy data and action-worthy meaning. Data cleaning, calculations in DAX, and dashboards all help create a better future for an organization, whether that future is full of opportunities or threats.
In a world full of data, the true power is in making an impact from the insights, and Power BI offers just that

Understanding Schemas and Data Modelling in Power BI

Barbara Morara — Mon, 02 Feb 2026 13:55:50 +0000

Introduction

Power BI is a powerful business intelligence tool that enables users to convert raw data into useful insights using interactive reports and dashboards. Nevertheless, the quality, performance, and accuracy of Power BI reports greatly rely on the way the data is modelled.
Data modelling is the process of structuring the data into tables, specifying the relationships between the tables, and selecting the correct schema. This article discusses the key concepts of data modelling for Power BI, which include schema, fact tables, dimension tables, relationships, star schema, and snowflake schema.

What Is Data Modelling in Power BI?

Data modelling in Power BI is defined as the process of organizing and structuring data in such a way that it can be easily analyzed and reported on. Data modelling in Power BI comprises:

Organizing the data in a logical table structure
Establishing relationships between the tables
Selecting the most appropriate schema
Ensuring the accuracy and efficiency of the data

Data modelling in Power BI helps in creating reports that are not only efficient but also easier to understand.

Fact Tables

A fact table holds data that is measurable and can be analyzed. This data is usually in numbers and is quite large in size.
Examples of data in a fact table:

Sales value
Number of units sold
Profit
Discount Fact tables usually have foreign keys that refer to dimension tables.

Dimension Tables
A dimension table holds data that describes the data in the fact table.

Examples of data in a dimension table:

Customer name
Product category
Date
Region or location Dimension tables are smaller and hold text data.

Relationships in Power BI

Relationships describe how tables are related in a data model. Power BI uses these relationships to merge data from different tables during analysis.

Common Types of Relationships

One-to-Many (1:*) – Most common (e.g., one customer → many sales)
One-to-One (1:1) – Rare
Many-to-Many (:) – Used cautiously

Relationship Direction

Single direction (recommended for simplicity and performance)
Both directions (can cause ambiguity if misused)

Well-defined relationships are essential to ensure correct calculations and avoid incorrect aggregations.

Schemas in Data Modelling

A schema is a method of organizing tables and relationships in a data model. The two most common schemas used in Power BI are Star Schema and Snowflake Schema.

Star Schema

A star schema is a data model that has a central fact table directly linked to multiple dimension tables. The data model looks like a star.

Features of Star Schema

There is only one fact table in the schema.
The dimension tables are not linked to each other.
The schema is simple and easy to understand.

Benefits of Star Schema

It has faster query execution times.
DAX calculations are simpler.
It is easier to maintain.
Recommended by Microsoft for Power BI.

Example

FactSales
DimCustomer
DimProduct
DimDate
DimRegion
DimRegion

Each dimension table is directly linked to the fact table.

Snowflake Schema

A snowflake schema is an extension of the star schema where the dimension tables are further normalized into multiple related tables.

Characteristics of Snowflake Schema

Dimension tables are divided into sub-dimension tables
More complex structure
Uses more relationships

Advantages of Snowflake Schema

Reduces data redundancy
Saves storage space
Disadvantages of Snowflake Schema
Slower performance compared to star schema
More complex relationships
Difficult for a beginner to understand

In Power BI, snowflake schemas are less preferred due to performance issues.

Feature	Star Schema	Snowflake Schema
Complexity	Simple	Complex
Performance	Faster	Slower
Number of Tables	Fewer	More
Ease of Use	Very Easy	Moderate to Difficult
Recommended in Power BI	Yes	Rarely

Why Good Data Modelling Is Critical in Power BI

Improves Performance
Data modeling can improve query speed and minimize relationships, hence improving user experience through quicker report loading.
Ensures Accurate Reporting
Good data modeling ensures that relationships are correct to avoid:
Double counting
Incorrect totals
Misleading business decisions
Simplifies DAX Calculations
Star schema modeling simplifies DAX formula writing, reading, and debugging.
Enhances Scalability
Data modeling can handle large volumes of data and can grow with business requirements.
Improves User Experience
Data modeling can ensure that users can understand data correctly and slice it correctly for easier decision-making.

Best Practices for Data Modelling in Power BI

Use star schema modeling
Dimension tables must be descriptive
Avoid many-to-many relationships
Use meaningful names
Validate relationships
Minimize bi-directional filter

Data modeling is a critical aspect of successful Power BI reporting. Understanding different schemas, fact tables, dimension tables, and relationships enables analysts to create successful data models for Power BI reporting. Among different schemas, star schema modeling is highly recommended for use in Power BI reporting because of its simplicity and ability to improve query speed. By using good data modeling practices, users can unlock the full potential of Power BI reporting.

Introduction to MS Excel for Data Analytics

Barbara Morara — Wed, 28 Jan 2026 15:58:39 +0000

Beginner-friendly guide

Microsoft Excel is one of the most widely used applications for data management. It is not necessary to be a programmer or a data scientist to begin analyzing data using Microsoft Excel. It has simple features such as tables, formulas, sorting, filtering, and charts that enable a beginner to understand the data, identify patterns, and make informed decisions.
This article is intended to provide information on the application of MS Excel as a simple data analytics tool.

What is Data Analytics?

Data analytics is the process of collecting, cleaning, organizing, and analyzing the data to extract valuable information.
In simple words, data analytics helps us find answers to the following questions:

How many were sold?
Which product performed the best?
What is the total, average, or maximum value? Excel assists us in finding the answers to these questions quickly and graphically.

Getting Started with MS Excel

When you start Excel, you see a sheet consisting of rows (numbered on the left side) and columns (lettered on the top). The intersection of each row and column is called a cell.
The most effective way to use Excel is when you organize your data in a table format.

This small data set will help us learn the basic concepts of data analysis.

Using Basic Formulas in Excel

One of the most powerful tools in Excel is the use of formulas. Formulas enable Excel to calculate data.

Calculating Total Sales

To calculate the total sales of each product:

Add a new column and name it Total
In the first cell of the Total column, type =B2*C2
Press Enter
Drag the formula down to calculate the totals for the other products

This multiplies the quantity by the price.

Using Common Excel Functions

SUM Function
The SUM function is used to add total sales.

This gives the total sales amount.

Average Function
The AVERAGE function finds the average value.

This is the average sales amount.

Sorting Data

Sorting data helps you arrange information in a useful way.
For instance, you can sort items from highest to lowest total sales as follows:

Select the data
Click Sort & Filter
Select Sort Largest to Smallest

This helps you identify the best-performing items.

Filtering Data
Filtering enables you to show data based on your needs.
Example: Display products with a quantity above 8.

Select your table
Click Filter
Use the drop-down arrows to apply conditions

Creating Simple Charts
Charts enable you to visualize data and understand insights easily.
To create a chart:

Select the data
Click Insert
Select a Column Chart or Bar Chart

Why Excel is Good for Beginners in Data Analytics

Excel is good for beginners in data analytics because:

It is easy to learn
No programming skills are required
Calculations are automatic
Data visualization is easy
It is used in most schools and work environments

Conclusion

Microsoft Excel is a powerful data analysis tool that is also suitable for beginners. It is easy to learn and use, and its mastery will provide a good foundation for more advanced data analysis tools in the future.
If you are a beginner in data analysis, then Excel is the best place to start.

A Beginner’s Guide to Version Control with Git: Tracking, Pushing, and Pulling Code

Barbara Morara — Sat, 17 Jan 2026 14:54:47 +0000

A problem that programmers, especially beginners, often encounter is:
you make changes to the code, something goes wrong, and you would like to go back to a previous state. That is where a “version control” tool comes in to help you.
In this article, you will learn:
-What version control is and why it is important
-Git Changes Tracking
-How to push code on GitHub
-How to pull code from GitHub

What Is Version Control?
Version Control is the process wherein changes that have been made to files are tracked. With versioning, you have the ability to:
-Record a history of your work
-See what was changed and when
-Restore previous versions of code
-Work together with others without overwriting their work

What Is Git?
Git is a version control system that you install and operate from your computer. It assists you with:
-Monitoring changes made in files
-Save copies of your project (known as "commits")
-Working Offline
-Manage your project effectively
Git operates locally. This means all the changes occur on your personal machine.

What Is GitHub?
GitHub is an online hosting service used for storing Git repositories. It enables you to:
-Online code backups
-Collaborate on projects with others
-Work together on team projects

Key Git Concepts for Beginners

Repository (Repo): A project folder managed with Git
Commit: A saved snapshot of changes
Staging Area: This refers to where changes to be committed to the project's source code will be
Push: Code Uploads to GitHub
Pull: Downloading updated code from GitHub

Tracking Changes with Git

To see the state of your files, use:

git status

This indicates which files have been updated or are ready to be committed.

To prepare files for saving, add them to the staging area:
git add .

To save the changes permanently, create a commit:
git commit -m "Describe what changed"

In a commit message, you should describe the update you implemented.

Pushing Code to GitHub

After committing, however, your changes exist only on your own computer.
To upload the files to GitHub,use:
git push

Pulling Code from GitHub

If an update is done on GitHub, the local project could become stale.
To update it, run:
git pull

Git can appear intimidating at first, but learning the fundamentals of version control, change tracking, pushing, and pulling code can make development a much safer and more organized process. Git can become a powerful and very simple tool if you practice using it frequently.

Happy coding