DEV Community: Grace Anyango

Statistics; Parametric & non-Parametric in Data science

Grace Anyango — Tue, 22 Sep 2026 07:22:50 +0000

Introduction

Statistics is an important part of data science because it helps data scientists collect, organize, analyze, interpret, and present data. It provides methods for understanding patterns in data and making conclusions based on evidence.

In data science, statistical tests are used to determine whether relationships or differences observed in data are meaningful or could have happened by chance. Two major categories of statistical tests are parametric tests and non-parametric tests.

Before performing a statistical test, it is also important to understand hypothesis testing, which helps researchers make decisions about a population using sample data.

What is Statistics?

Statistics is the branch of mathematics concerned with collecting, organizing, analyzing, interpreting, and presenting data.

Statistics is widely used in:

Data science
Business
Healthcare
Banking and finance
Education
Marketing
Government
Scientific research

In data science, statistics helps transform raw data into meaningful information that can support decision-making.

Descriptive and Inferential Statistics

Statistics can generally be divided into descriptive statistics and inferential statistics.

A.Descriptive Statistics

Descriptive statistics are used to summarize and describe the main features of a dataset.

They help answer questions such as:

What is the average?
What is the highest or lowest value?
How spread out is the data?
What is the most common value?

Common descriptive statistical measures include:

Mean – the average value.
Median – the middle value when data is arranged in order.
Mode – the value that occurs most frequently.
Range – the difference between the highest and lowest values.
Standard deviation – measures how spread out the data is around the mean.
Variance – measures the amount of variation in a dataset.

B.Inferential Statistics

Inferential statistics are used to make conclusions or predictions about a larger population based on a sample of data.

Inferential statistics commonly involve:

Hypothesis testing
Confidence intervals
Correlation
Regression
Statistical tests

Hypothesis Testing

Hypothesis testing is a statistical method used to determine whether there is enough evidence to support a claim about a population.

A researcher begins with a question or claim and develops two hypotheses:

Null Hypothesis (H₀)

The null hypothesis states that there is no significant difference, relationship, or effect between the variables being studied.

Alternative Hypothesis (H₁ or Hₐ)

The alternative hypothesis states that there is a significant difference, relationship, or effect.

1.Parametric Tests

Parametric tests are statistical tests that make certain assumptions about the population or distribution of the data. They commonly assume that the data follows a particular distribution, such as a normal distribution, and they often involve parameters such as the mean and standard deviation.

Parametric tests are commonly used when the data is numerical and meets the required assumptions.

Common Parametric Tests

1.T-Test

A t-test is used to determine whether there is a significant difference between means.

Common types include:

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

2.ANOVA

ANOVA (Analysis of Variance) is used to compare the means of three or more groups.

3.Pearson Correlation

Pearson correlation measures the strength and direction of a linear relationship between two numerical variables.

The correlation coefficient ranges from -1 to +1:

+1 = perfect positive relationship
0 = no linear relationship
-1 = perfect negative relationship

4.Linear Regression

Linear regression is used to examine the relationship between variables and can be used to predict one variable based on another.

Advantages of Parametric Tests

High statistical power – They can detect differences or relationships effectively when their assumptions are satisfied.
Use numerical information – They make effective use of the actual values in a dataset.
Useful for many analyses – They can be applied to comparisons, correlations, and predictions.
Well-established methods – They are widely used in research and data science.
Can provide precise results – When assumptions are met, parametric tests can provide reliable estimates and conclusions.

5.Non-Parametric Tests

Non-parametric tests are statistical tests that do not require the same strict assumptions about the distribution of the data as parametric tests.

They are particularly useful when:

Data does not follow a normal distribution.
The sample size is small.
Data contains outliers.
Data is ordinal or ranked.
The assumptions required for a parametric test are not satisfied.

Instead of relying heavily on means and standard deviations, many non-parametric tests work with ranks or the order of observations.

Common Non-Parametric Tests

1.Mann-Whitney U Test

The Mann-Whitney U test is used to compare two independent groups when the assumptions of an independent t-test are not appropriate.

2.Wilcoxon Signed-Rank Test

The Wilcoxon signed-rank test is used to compare two related or paired sets of observations.

3.Kruskal-Wallis Test

The Kruskal-Wallis test is used to compare three or more independent groups.
It is commonly considered a non-parametric alternative to one-way ANOVA.

4.Friedman Test

The Friedman test is used to compare three or more related or repeated groups.

5.Spearman Rank Correlation

Spearman rank correlation measures the strength and direction of a relationship between variables based on their ranks.
It is useful when data is ordinal or when the assumptions required for Pearson correlation are not appropriate.

Advantages of Non-Parametric Test

Fewer distribution assumptions – They do not require data to follow a specific distribution in the same way as many parametric tests.
Useful for ordinal data – They can work well with ranked or ordered data.
Less affected by outliers – Rank-based methods can be less sensitive to extreme values.
Useful with small samples – They can be appropriate when sample sizes are small, depending on the test and study design.
Flexible – They can be useful when the assumptions of parametric tests are not satisfied.

Parametric vs Non-Parametric Tests

Feature	Parametric Tests	Non-Parametric Tests
Distribution assumptions	Usually stronger	Usually fewer
Data	Often numerical/continuous	Often ordinal, ranked, or non-normal numerical data
Common measure	Mean	Ranks/medians
Outliers	Can have greater influence	Often less sensitive
Examples	t-test, ANOVA, Pearson correlation	Mann-Whitney U, Wilcoxon, Kruskal-Wallis, Spearman
Statistical power	Often higher when assumptions are met	Can be preferable when assumptions are violated

The choice between the two depends on the type of data, research question, sample size, and whether the assumptions of the test are satisfied.

Unsupervised learning in ML

Grace Anyango — Mon, 21 Sep 2026 06:04:39 +0000

Introduction

Unsupervised learning is a type of machine learning where a computer learns from data that does not have predefined labels or answers. Instead of being told what the correct result is, the algorithm studies the data and identifies patterns, groups, or relationships on its own.

Unsupervised learning is useful when there is a large amount of data but no labelled information available. It helps organizations discover hidden patterns and gain a better understanding of their data.

The two major areas of unsupervised learning discussed in this article are:

Clustering
Dimensionality Reduction

1.CLUSTERING

Clustering is an unsupervised learning technique that groups similar data points together. Data points within the same group, known as a cluster, are more similar to each other than to data points in other groups.

Uses of Clustering

Clustering can be used for:

Customer segmentation – grouping customers according to their behaviour or characteristics.
Market research – identifying different groups within a market.
Image analysis – grouping similar images or parts of images.
Fraud detection – identifying unusual patterns in transactions.
Document classification – grouping documents with similar topics.
Recommendation systems – identifying users with similar interests.
Healthcare analysis – grouping patients with similar characteristics or patterns.

HIERARCHICAL CLUSTERING

Hierarchical clustering is a clustering technique that creates a hierarchy of groups. Instead of simply dividing data into a fixed number of clusters, it shows how individual data points and groups are related at different levels.

There are two main approaches to hierarchical clustering:

I).Agglomerative Clustering

Agglomerative clustering is a bottom-up approach.

It starts with each data point as its own individual cluster. The algorithm then repeatedly combines the most similar clusters until all the data points form one large cluster or the desired grouping is reached.

Process:

Individual Data Points → Small Clusters → Larger Clusters → One Large Cluster

Agglomerative clustering is the more commonly used approach to hierarchical clustering.

II).Divisive Clustering

Divisive clustering is a top-down approach.

It begins with all data points in one large cluster. The algorithm then repeatedly divides the cluster into smaller groups until individual groups are formed or the desired number of clusters is reached.

Process:

One Large Cluster → Smaller Clusters → Individual Groups

Summary

Approach	Direction	Starting Point
Agglomerative	Bottom-up	Individual data points
Divisive	Top-down	One large cluster

DENDROGRAM

A dendrogram is a tree-like diagram used to display the results of hierarchical clustering.

It shows how individual data points or groups are combined or divided at different levels.

The height at which two groups are connected represents how different or distant they are. By choosing a particular level on the dendrogram, we can determine how many clusters to create.

Example of a simple dendrogram

              ┌───────────────┐
              │               │
          ┌───┴───┐       ┌───┴───┐
          │       │       │       │
          A       B       C       D

Here, A and B are grouped together, while C and D form another group.

Importance of a Dendrogram

Dendrograms help users:

Understand how data points are related.
Visualize hierarchical clusters.
Decide how many clusters should be created.
Identify groups with similar characteristics.

DBSCAN

DBSCAN stands for Density-Based Spatial Clustering of Applications with Noise.

It is a clustering algorithm that groups data points based on how closely packed they are. It can identify areas with many data points while treating points that do not belong to a dense group as noise or outliers.

Unlike some clustering methods, DBSCAN does not require the user to specify the exact number of clusters beforehand.

How DBSCAN Works

DBSCAN looks for areas where data points are close together.

It generally identifies three types of points:

Core points – points located within a sufficiently dense area.
Border points – points near a dense cluster but not dense enough to be core points.
Noise points – points that do not belong to any cluster.

Advantages of DBSCAN

Can find clusters with different shapes.
Can identify outliers and noise.
Does not require the number of clusters to be specified beforehand.
Useful for spatial and location-based data.

Disadvantages of DBSCAN

Its performance can be affected when clusters have very different densities.
Choosing suitable parameter values can be difficult.
It may not work well with very high-dimensional data without appropriate preparation.

2.DIMENSIONALITY REDUCTION

Dimensionality reduction is the process of reducing the number of variables or features in a dataset while trying to preserve the most important information.

Dimensionality reduction transforms the data into fewer dimensions while attempting to retain its important patterns.

Benefits of Dimensionality Reduction

Makes large datasets easier to analyze.
Reduces computational requirements.
Can make data easier to visualize.
Helps remove unnecessary or redundant information.
Can improve the performance of some machine learning algorithms.

PRINCIPAL COMPONENT ANALYSIS (PCA)

Principal Component Analysis (PCA) is one of the most widely used dimensionality reduction techniques.

PCA transforms a dataset with many variables into a smaller number of new variables called principal components.

The principal components are created so that they capture as much of the important variation in the original data as possible.

Uses of PCA

PCA can be used for:

Reducing the number of features.
Visualizing high-dimensional data.
Removing redundant information.
Simplifying datasets before further analysis.
Improving computational efficiency.

Advantages of PCA

Reduces the number of variables.
Makes complex datasets easier to visualize.
Can reduce redundancy in data.
Can make some machine learning processes faster.

Disadvantages of PCA

The new components can be difficult to interpret.
Some information may be lost during the reduction.
The results can be affected by the scale of the variables.
It may not be suitable when every original variable has important meaning.

APPLICATIONS OF UNSUPERVISED LEARNING

1.Marketing

Businesses can use clustering to divide customers into groups based on their purchasing habits, interests, or behaviour. This helps businesses understand different customer groups.

2.Banking and Finance

Unsupervised learning can help identify unusual transaction patterns and discover groups of customers with similar financial behaviour.

3.Healthcare

Healthcare organizations can analyze patient data to identify groups with similar characteristics or discover patterns in medical information.

4.Retail

Retail businesses can analyze purchasing behaviour to identify groups of customers and discover which products are commonly purchased together.

5.Image Processing

Clustering can be used to group similar pixels or identify patterns within images. Dimensionality reduction can also help simplify complex image data.

6.Cybersecurity

Unsupervised learning can identify unusual patterns in network activity that may require further investigation.

7.Social Media

Organizations can analyze large amounts of social media information to identify groups, topics, trends, or patterns in user behavior.

8.Manufacturing

Manufacturers can use unsupervised learning to identify unusual machine behaviour and discover patterns that may indicate equipment problems.

Introduction to Machine Learning

Grace Anyango — Sun, 20 Sep 2026 05:59:39 +0000

WHAT IS MACHINE LEARNING?

Machine learning (ML) is a branch of artificial intelligence that enables computers to learn from data and make predictions or decisions based on what they have learned.

Basic Machine Learning Process

Data → Training → Learning Patterns → Prediction/Decision

Machine learning therefore depends heavily on data, because the quality and quantity of training data can affect the performance of the model.

TYPES OF MACHINE LEARNING

There are three main types of machine learning:

Supervised Learning
Unsupervised Learning
Reinforcement Learning

Each type learns from data differently and is suitable for different problems.

1.SUPERVISED LEARNING

Supervised learning is a type of machine learning where a model is trained using data that already contains the correct answers, known as labels.

The model learns the relationship between the input data and the known output. It can then use what it has learned to make predictions about new data.

Supervised learning is commonly used for:

Predicting prices.
Detecting spam emails.
Predicting customer behaviour.
Classifying images.
Detecting fraudulent transactions.

Advantages

Can make accurate predictions when good-quality labelled data is available.
Performance can be measured using known answers.
Useful for classification and prediction problems.

Disadvantages

Requires labelled training data, which can be expensive or time-consuming to prepare.
Poor-quality training data can lead to poor predictions.
The model may perform poorly when it encounters situations that are very different from its training data.

2.UNSUPERVISED LEARNING

Unsupervised learning is a type of machine learning where the model works with data that does not have predefined labels or answers.

The system attempts to discover patterns, groups, or relationships within the data on its own.

Common Uses

Unsupervised learning can be used for:

Customer segmentation.
Finding patterns in large datasets.
Grouping similar products.

Advantages

Does not require labelled data.
Can discover patterns that may not be obvious to humans.
Useful for exploring large datasets.

Disadvantages

Results can be difficult to interpret.
It may not always be clear whether the groups discovered are meaningful.
Evaluating the accuracy of the results can be more difficult.

3.REINFORCEMENT LEARNING

Reinforcement learning is a type of machine learning in which an agent learns by interacting with an environment.

The system performs an action and receives feedback in the form of a reward or penalty. Over time, it learns which actions are more likely to produce desirable results.

Common Uses

Reinforcement learning can be used in:

Robotics.
Game-playing systems.
Autonomous systems.
Resource management.
Some recommendation and optimization problems.

Advantages

Can learn through experience.
Does not always require labelled training examples.
Useful for problems involving sequences of decisions.
Can improve its strategy through repeated interaction.

Disadvantages

Training can require a large number of attempts.
Training can be computationally expensive.
Designing suitable rewards can be difficult.

COMPARISON OF THE THREE TYPES

Type	How It Learns	Example	Main Advantage	Main Disadvantage
Supervised Learning	Learns from labelled data	Predicting house prices	Good for prediction	Requires labelled data
Unsupervised Learning	Finds patterns in unlabelled data	Customer grouping	Discovers hidden patterns	Results can be difficult to interpret
Reinforcement Learning	Learns through rewards and penalties	Robot learning movement	Learns through experience	Can require extensive training

APPLICATIONS OF MACHINE LEARNING IN INDUSTRIES

1.Healthcare

Applications include:

Supporting medical image analysis.
Predicting certain health risks.
Analyzing patient records.
Supporting drug research.
Monitoring patients.

2.Banking and Finance

Applications include:

Fraud detection.
Credit risk analysis.
Detecting unusual transactions.
Customer segmentation.
Financial forecasting.

3.Retail and E-Commerce

Applications include:

Product recommendations.
Customer segmentation.
Sales forecasting.
Inventory management.
Price analysis.

4.Manufacturing

Applications include:

Predictive maintenance.
Quality control.
Production forecasting.
Detecting equipment problems.
Optimizing production processes.

5.Transportation

Applications include:

Traffic prediction.
Route optimization.
Demand forecasting.
Driver-assistance systems.
Fleet management.

Understanding Data Modeling in Power Bi

Grace Anyango — Thu, 17 Sep 2026 18:41:40 +0000

Introduction

Data modeling in Power BI is the process of organizing data from different tables and connecting them so that the data can be analyzed effectively. A good data model makes it easier to create accurate reports, dashboards, charts, and calculations.

WHAT IS DATA MODELING?

Data modeling is the process of structuring and organizing data so that different pieces of information can work together.

Importance of Data Modeling

Data modeling helps to:

Organize large amounts of data.
Connect related tables.
Reduce unnecessary duplication of information.
Improve the accuracy of calculations.
Make reports easier to create and understand.
Improve the performance of Power BI reports.

Summary

Data modeling creates a structured connection between different tables so that Power BI can analyze them as a unified dataset.

JOINS IN POWER BI

A join combines data from two or more tables based on a related column.

Power BI allows users to join tables using Power Query through the Merge Queries feature.

Joins are particularly useful when information is stored in separate tables but needs to be combined for analysis.

Summary

Joins combine information from different tables using a common column.

THE SIX SQL JOINS

SQL commonly describes six types of joins. These are also useful concepts to understand when working with data in Power BI.

INNER JOIN

An INNER JOIN returns only the records that have matching values in both tables.

Use: When you only need records that exist in both tables.

LEFT JOIN

A LEFT JOIN returns all records from the left table and matching records from the right table.

If there is no match in the right table, the result will contain a blank value for that table.

Use: When you want to keep every record from the main or left table.

RIGHT JOIN

A RIGHT JOIN returns all records from the right table and matching records from the left table.

If there is no match in the left table, the missing values appear as blanks.

Use: When the right table contains the records you want to keep.

FULL OUTER JOIN

A FULL OUTER JOIN returns all records from both tables.

It includes matching records as well as records that do not have a match.

Use: When you want to see every record from both tables.

CROSS JOIN

A CROSS JOIN combines every row in one table with every row in another table.

For example, if one table has 3 rows and another has 4 rows, a cross join can produce 12 combinations.

Use: When every possible combination between two sets of data is required.

SELF JOIN

A SELF JOIN joins a table to itself.

It can be useful when records within the same table are related to one another.

For example, an employee table could contain both employees and their managers. The manager's ID could be matched with another employee's ID within the same table.

Use: When relationships exist between records in the same table.

Summary of the Six Joins

Join	Main Purpose
INNER JOIN	Returns matching records
LEFT JOIN	Keeps all records from the left table
RIGHT JOIN	Keeps all records from the right table
FULL OUTER JOIN	Keeps all records from both tables
CROSS JOIN	Creates every possible combination
SELF JOIN	Joins a table to itself

HOW TO CREATE JOINS IN POWER BI

In Power BI, joins can be created in Power Query using the Merge Queries option.

Steps

Step 1: Open Power BI Desktop

Step 2: Open Power Query Editor

Step 3: Select a table

Step 4: Select Merge Queries

Step 5: Select the second table

Step 6: Select the matching column

Step 7: Choose the Join Kind

Step 8: Select OK

Step 9: Expand the new column

Step 10: Apply the changes

Summary

Creating a join in Power BI involves selecting two tables, choosing their matching columns, selecting a join type, and then expanding the required information.

RELATIONSHIPS IN POWER BI

A relationship connects two tables in the Power BI data model. Unlike a merge, which combines columns into one table, a relationship allows separate tables to remain separate while Power BI understands how they are connected.

Types of Relationships

Power BI commonly uses the following relationship types:

One-to-Many (1:*)

One record in one table can be related to many records in another table.

One-to-One (1:1)

One record in one table is related to only one record in another table.

Many-to-Many (:)

Many records in one table can be related to many records in another table.

Creating a Relationship in Power BI

Relationships can be created by:

Opening Model view.
Selecting a column in one table.
Dragging it to the related column in another table.
Choosing the appropriate cardinality, such as one-to-many.
Setting the filter direction when required.
Selecting OK.

Power BI then uses the relationship when calculating and displaying information in reports.

Summary

Relationships connect tables without necessarily combining them. They allow Power BI to understand how information in different tables is related.

DIFFERENCE BETWEEN JOINS AND RELATIONSHIPS

Although joins and relationships both connect data, they work differently.

Joins	Relationships
Usually combine tables during data preparation	Connect tables in the data model
Often performed in Power Query	Usually created in Model view
Can create one combined table	Keep tables separate
Useful for transforming and preparing data	Useful for analyzing related tables
Uses matching columns	Uses related columns and defined cardinality

Example

A join could combine Customer Name and Customer Sales into one table.

A relationship could keep Customers and Sales as separate tables while allowing Power BI to analyze sales by customer.

Conclusion

Data modeling is an important part of working with Power BI because it organizes data and allows different tables to work together. Joins can be used to combine information from different tables, while relationships connect tables within the Power BI data model.

Introduction to SQL :DDL,DML & Data Quering

Grace Anyango — Thu, 17 Sep 2026 17:45:58 +0000

Introduction

SQL stands for Structured Query Language. It is a programming language used to communicate with and manage data stored in relational databases. SQL allows users to create databases and tables, add and modify information, search for specific records, and organize data.

1. DDL AND DML

SQL commands are divided into different categories. Two important categories are DDL and DML.

DDL – Data Definition Language

DDL stands for Data Definition Language. It is used to create and modify the structure of a database and its objects, such as tables.

Common DDL commands include:

CREATE – creates a new database object, such as a table.
ALTER – changes the structure of an existing table.
DROP – permanently removes a database object.
TRUNCATE – removes all records from a table while keeping the table structure.

Example:

CREATE TABLE Students (
    StudentID INT,
    Name VARCHAR(50),
    Age INT
);

This command creates a table called Students with three columns.

DML – Data Manipulation Language

DML stands for Data Manipulation Language. It is used to add, change, and remove data stored in database tables.

Common DML commands include:

INSERT – adds new records.
UPDATE – changes existing records.
DELETE – removes records.

Example:

INSERT INTO Students (StudentID, Name, Age)
VALUES (1, 'Grace', 20);

This adds a new student record to the table.

Summary

DDL manages the structure of the database, while DML manages the data stored inside the database.

THE WHERE CLAUSE

The WHERE clause is used in SQL to specify a condition. It allows us to select only the records that meet a particular requirement.

For example:

SELECT *
FROM Students
WHERE Age = 20;

This query displays students whose age is 20.

The WHERE clause is useful when working with large databases because it allows specific records to be selected instead of displaying everything.

Operators Used with the WHERE Clause

1. Comparison Operators

These operators compare two values.

Operator	Meaning	Example
`=`	Equal to	`Age = 20`
`<>` or `!=`	Not equal to	`Age <> 20`
`>`	Greater than	`Age > 18`
`<`	Less than	`Age < 18`
`>=`	Greater than or equal to	`Age >= 18`
`<=`	Less than or equal to	`Age <= 18`

2. Logical Operators

Logical operators combine or modify conditions.

AND – both conditions must be true.
OR – at least one condition must be true.
NOT – reverses a condition.

Example:

SELECT *
FROM Students
WHERE Age >= 18 AND Age <= 25;

This selects students between the ages of 18 and 25.

3. BETWEEN

BETWEEN selects values within a specified range.

SELECT *
FROM Students
WHERE Age BETWEEN 18 AND 25;

4. IN

IN checks whether a value matches any value in a given list.

SELECT *
FROM Students
WHERE Age IN (18, 20, 22);

5. LIKE

LIKE is used to search for a particular pattern in text.

SELECT *
FROM Students
WHERE Name LIKE 'G%';

The % means that any number of characters can follow G.

6. IS NULL

IS NULL is used to find records where a column has no value.

SELECT *
FROM Students
WHERE Age IS NULL;

Summary

The WHERE clause helps filter database records. Its common operators include =, <>, !=, >, <, >=, <=, AND, OR, NOT, BETWEEN, IN, LIKE, and IS NULL.

CASE WHEN

CASE WHEN is used in SQL to perform conditional logic. It works similarly to an IF-ELSE statement in programming.

It checks a condition and returns a result when that condition is true.

Basic Structure

CASE
    WHEN condition THEN result
    ELSE result
END

Example

Suppose we have students and their marks. We can use CASE WHEN to classify their performance:

SELECT Name, Marks,
    CASE
        WHEN Marks >= 70 THEN 'Excellent'
        WHEN Marks >= 50 THEN 'Pass'
        ELSE 'Fail'
    END AS Performance
FROM Students;

The query creates a new column called Performance.

For example:

Name	Marks	Performance
Grace	78	Excellent
Brian	56	Pass
Jane	42	Fail

Uses of CASE WHEN

CASE WHEN can be used to:

Categorize data.
Create labels.
Group records based on conditions.
Replace complicated conditional calculations.
Make query results easier to understand.

Summary

CASE WHEN allows SQL to make decisions based on conditions and return different results depending on the data.

DATA QUERYING

Data querying is the process of requesting specific information from a database. SQL is mainly used for querying data through the SELECT statement.

Basic SELECT Query

SELECT *
FROM Students;

The * means that all columns should be displayed.

Selecting Specific Columns

Instead of selecting everything, we can select particular columns:

SELECT Name, Age
FROM Students;

This displays only the Name and Age columns.

Filtering Data

The WHERE clause can be used when querying specific records:

SELECT Name, Age
FROM Students
WHERE Age >= 18;

Sorting Data

The ORDER BY clause sorts query results.

SELECT Name, Marks
FROM Students
ORDER BY Marks DESC;

DESC sorts from highest to lowest, while ASC sorts from lowest to highest.

Combining Querying Techniques

SQL allows several clauses to be used together:

SELECT Name, Marks
FROM Students
WHERE Marks >= 50
ORDER BY Marks DESC;

This query:

Selects the student's name and marks.
Displays only students who scored 50 or above.
Arranges the results from the highest mark to the lowest.

Summary

Data querying allows users to retrieve useful information from databases. Common SQL commands and clauses used for querying include SELECT, FROM, WHERE, ORDER BY, GROUP BY, and HAVING.

Conclusion

Learning SQL is especially useful in data analytics because it allows analysts to work directly with large amounts of structured data and extract the information needed for decision-making.

How to publish a Power BI report and embed it on a website

Grace Anyango — Thu, 17 Sep 2026 17:18:14 +0000

Introduction

Power BI is a data visualization and business intelligence tool developed by Microsoft. It allows users to connect to different sources of data, analyze information, and create interactive reports and dashboards. Power BI reports can be published online and, depending on the type of embedding used, displayed on a website so that users can view the information in an interactive way.

Publishing a report makes it available through the Power BI service, while embedding allows the report to appear within another website or application.

1. Creating a Workspace

A workspace is an area in Power BI where reports, dashboards, and related data are stored and managed. Creating a workspace is an important first step before publishing a report.

Steps:

Sign in to Power BI.
Open the Workspaces section.
Select New workspace.
Enter a suitable name for the workspace.
Select Create or Apply.
The new workspace will now be available for storing and managing reports.

Summary: A workspace provides a central place where Power BI reports and other related content can be stored and managed.

2. Uploading and Publishing a Report

A report can be created using Power BI Desktop and then published to the Power BI service. Publishing transfers the report and its data model to the selected workspace.

Steps:

Open Power BI Desktop.
Create or open the report you want to publish.
Check that the charts, tables, and data are correct.
Select Publish from the Home ribbon.
Sign in to your Power BI account if required.
Select the workspace where you want to publish the report.
Select Publish or Select.
Wait for the publishing process to finish.
Open the report in the Power BI service to confirm that it has been published successfully.

Summary: Publishing moves the report from Power BI Desktop into an online Power BI workspace where it can be accessed and shared.

3. Steps to Publishing a Report

After publishing, it is important to check and prepare the report before sharing it with other users.

Steps:

Open the published report in the Power BI service.
Check that all visuals and pages are displaying correctly.
Confirm that the data is accurate and up to date.
Check the report's sharing and access permissions.
Make any necessary changes to the report.
Save or republish the report if changes are made.
Decide how the report will be shared, such as through a Power BI app or an appropriate embedding method.

Summary: After publishing, the report should be checked for accuracy, appearance, permissions, and accessibility before it is shared.

4. Embedding the Report on a Website

Embedding means displaying a Power BI report inside a website. There are different methods depending on whether the report contains public or private information.

For a public report, Power BI's Publish to web feature can generate an embed code that can be placed in a website using HTML or an iframe. However, Microsoft warns that Publish to web makes the report publicly accessible without authentication, so it should only be used for information that is safe to make public.

Steps for a Public Embed:

Open the report in the Power BI service.
Select File → Embed report → Publish to web.
Review the warning about public access.
Select Create embed code.
Confirm that the report is suitable for public viewing.
Select Publish.
Copy the HTML embed code provided by Power BI.
Open the website's HTML editor.
Paste the embed code into the appropriate section of the webpage.
Save and preview the website to make sure the report appears correctly.

Summary: Embedding allows an interactive Power BI report to appear directly on a webpage. Public embedding should only be used when the report and its data are appropriate for public access.

For private or organizational reports, a secure embedding method should be used instead of Publish to web. Microsoft provides options such as Embed for your organization and Power BI Embedded, which can enforce authentication and permissions.

Conclusion

Publishing and embedding a Power BI report makes it easier to share data and present information in an interactive format. The general process involves creating a workspace, creating and publishing the report, checking the published report, and choosing an appropriate method for embedding it on a website. For public information, Publish to web can provide an easy way to embed a report, while private information requires a secure embedding method. By following these steps, users can effectively share Power BI reports and make data easier to access and understand.

Introduction to Python for Data Analytics

Grace Anyango — Thu, 17 Sep 2026 10:16:43 +0000

What Is Data Analytics and How Is Python Involved?

Data analytics is the process of collecting, organizing, cleaning, examining, and interpreting data to find useful patterns and information.

Python is a programming language that is widely used in data analytics. It allows analysts to work with large datasets, perform calculations, clean data, create visualizations, and automate repetitive tasks. Python also has libraries such as Pandas, NumPy, Matplotlib, and Seaborn that provide tools specifically designed for data analysis.

Why Python Is Highly Recommended in Data Analytics

Python is highly recommended for data analytics for several reasons:

.Easy to learn: Python has simple and readable syntax, making it suitable for beginners.

.Powerful libraries: Libraries such as Pandas and NumPy make data manipulation and calculations easier.

.Data visualization: Libraries such as Matplotlib and Seaborn help analysts create charts and graphs.

.Handles large datasets: Python can process and analyze large amounts of information efficiently.

.Automation: Python can automate repetitive data-processing tasks, saving time.

.Flexible: It can be used for data analysis, machine learning, artificial intelligence, and other tasks.

.Large community: Many tutorials, learning resources, and community-supported tools are available.

Core Principles of Python in Data Analytics

Some important principles of using Python for data analytics include:

a) Variables and Data Types

Variables are used to store information, while data types describe the kind of information being stored, such as numbers, text, and Boolean values.

b) Functions

Functions are reusable blocks of code that perform specific tasks. They help make programs organized and easier to manage.

c) Libraries

Python libraries provide ready-made tools for performing different tasks. For example, Pandas is used for data manipulation, while NumPy is useful for numerical calculations.

d) Conditional Statements and Loops

Conditional statements allow Python to make decisions, while loops allow it to repeat tasks automatically.

Analyzing Data Files

Python can be used to open and analyze different types of data files, including CSV and Excel files. With libraries such as Pandas, analysts can import data, examine columns and rows, calculate statistics, sort information, and identify patterns.

For example, a business can use Python to analyze a sales file and determine which products are selling the most.

Data Cleaning

Data cleaning involves preparing raw data for analysis. Python can help analysts:

.Remove duplicate records.
.Handle missing information.
.Correct errors.
.Change data into the correct format.
.Remove unnecessary information.
.Organize data into a consistent structure.

Clean data is important because inaccurate or incomplete data can lead to incorrect conclusions.

Data Visualization

Data visualization involves presenting information using charts and graphs. Python provides libraries such as Matplotlib and Seaborn for creating visual representations of data.

For example, an analyst can use a bar chart to compare sales between different products or a line graph to show how sales change over time. Visualizations make complex information easier to understand.

Python in Real-World Data Analytics

Python is used in many real-world industries. Some examples include:

1.Banking: Analyzing transactions and identifying unusual patterns.

2.Healthcare: Studying patient and medical data to identify trends.

3.Business: Analyzing sales, customers, and business performance.

4.Education: Examining student performance and attendance.

5.Marketing: Studying customer behavior and campaign results.

6.Finance: Analyzing financial information and market data.

These applications show how Python can help organizations turn large amounts of data into useful information.

Advantages of Python in Data Analytics

Python has several advantages, including:

I).It is easy to learn and use.
II).It has many powerful data analytics libraries.
III).It can automate repetitive tasks.
IV).It supports data visualization.
V).It can work with different types of data.
VI).It is useful for both small and large data projects.
VII).It can be combined with machine learning and artificial intelligence.

Disadvantages of Python in Data Analytics

Although Python is useful, it also has some disadvantages:

I).It can use more memory when working with very large datasets.
II).It may be slower than some programming languages for certain computational tasks.
III).Beginners may need time to understand its libraries and data-analysis concepts.
IV).Some advanced analytics tasks require knowledge beyond basic Python.
V).Managing large Python projects can become complicated without good organization.

Conclusion

Python is an important and powerful tool for data analytics. It helps analysts import and analyze data, clean errors, perform calculations, create visualizations, and automate tasks. Its simple syntax and wide range of libraries make it suitable for beginners as well as professional data analysts. Python is also used in many industries, including banking, healthcare, education, marketing, and business. Although it has some disadvantages, its flexibility and powerful data-analysis capabilities make Python a valuable skill for anyone interested in data analytics.

How excel in used in the real world

Grace Anyango — Thu, 17 Sep 2026 08:37:54 +0000

What is excel

Microsoft Excel is a spreadsheet program used to enter, organize, calculate, analyze, and present data. It uses rows and columns to arrange information in cells. Excel provides different tools such as formulas, functions, tables, filters, and charts that make it easier to work with data.

Real-World Uses of Excel

Excel is used in many areas of everyday life and business. Some of its major uses include:

Data Cleaning

Excel helps users organize, correct, and remove errors from data so that it can be used for accurate analysis.

Business Forecasting

Excel helps businesses predict future sales, expenses, profits, and trends using past and current data.

Business Calculations

Excel makes it easy to perform calculations using formulas and functions, such as totals, averages, percentages, and profits.

Tracking of Expenses

Excel can be used to record and monitor income and expenses, helping individuals and businesses manage their finances.

Data Analysis Using Charts

Excel converts data into charts and graphs, making trends, comparisons, and patterns easier to understand.

Conclusion

Excel is a powerful and useful tool for real-world data analysis. It helps users organize and clean data, perform calculations, forecast business trends, track expenses, and present information through charts and graphs. Because it is easy to use and suitable for many different tasks, Excel remains an important tool for students, businesses, and data analysts. Learning Excel can therefore provide valuable skills for both education and the workplace.