DEV Community: ekitindi

The Ultimate Guide to Data Analytics

ekitindi — Tue, 27 Aug 2024 07:40:06 +0000

Very many companies are currently collecting a lot of data from their business activities, and are sitting on a gold mine of data that could help propel their businesses to the next level. This data, to the companies that are unaware, is collected in raw form and could help propel their businesses to the next level.

So What is Data Analytics?

Simply put, data analytics is the process of analyzing raw data to draw out meaningful, actionable insights, which are then used to inform and drive smart business decisions.

The primary objective of data analytics is to address specific questions or challenges that are relevant to an organization to drive better business outcomes.

The demand for data analysts is constantly rising, with a report in 2020 showing that it is one of the seven high-growth emerging professionals, at 41% per year.

Its good to note that there is a difference in a data analyst and data scientist, mainly:

What they DO with data
A data analyst will look at specific problems or challenges to be addressed by the business. They do this by collecting the data, identify trends and patterns and visualise their finginds in form of charts, graphs etc. to the business stakeholders.
Data Scientists on the other hand consider what the business should be asking. They devise models and algorithms based on the data, running custom analysis, writing algorithms and devising predictive models to elp the business.
Data analysts ofen work on request to solve specific questions at the time, nad data scientists build systems to to automate and optimize the overall business functions.
Tools and skills
Analysts are typically proficient in spreadsheets, SQL, R, Python, SAS and data visualisation(PowerBI and Tableau); while data scientists are good with most of the above, including object-oriented programming, statistics and machine learning.

Importance Data Analysis

Data analysis is important in improving how we work, make choices and solve problems.

it is applied in:

Measuring Performance: To measure organizational performance, employee productivity, and the effectiveness of strategies.
Improving Efficiency: Enhances efficiency by identifying areas for improvement and optimizing processes.
Better Decision-Making: Helps in making smarter and more informed decisions by analyzing patterns and trends in data.

among others.

Steps of Data Analytics

Business needs: What question or challenge you hope to solve. At this stage, you’ll take a clearly defined problem and come up with a relevant question or hypothesis you can test.
Collecting Data: Getting data from different places like websites, surveys, or business records. you will use tools like SQL here.
Organise the data: Often the most time consuming and a very crucial step, it involves cleaning up the data by fixing mistakes, removing duplicates, and putting it in order so it's easier to look at.
Analyzing Data: Using math and computer programs to find patterns, trends, or interesting facts in the data.
Sharing your findings: Understanding what the data is telling us and using that information to make choices or plan what to do next. Its also a good times to mention your limitations and what fusrther analysis can be conducted.

The Types of Data Anaytics

Descriptive Analytics: Looks at past data to tell you what happened before.
Diagnostic Analytics: Tries to figure out why something happened.
Predictive Analytics: Uses past data to guess what might happen in the future.
Prescriptive Analytics: Suggests what you should do based on the data analysis.

Industry Application of Data Analytics

Data Analysis is applied to a wide range of idustries around the world. Data analysts collaborate with different teams all to determine organisational goals and needs, gather, analyze and report their findings.

Below are some examples of areas of application:

Technology: to analyse which software functions users like.
Marketing: to asses effectiveness of past campaigns to see what strategies to replicate or change.
Insurance: to improve processes, generate leads and increase customer retention as well as analyze a customer’s risk and detect potential fraud.

Data Analytics is major factor driving the future of technology given that it is all around us. With the increasing rise in remote and hybrid workiing, many people can consider data analytics as a possible career path with flexibility and independence.

References:
1. An Introduction to Data Analytics

2. What Is Data Analytics: The Ultimate Guide

3. A Complete Guide to Data Analytics

4. What is Data Analytics? A Complete Guide for Beginners

Feature Engineering

ekitindi — Sun, 18 Aug 2024 17:21:35 +0000

What is Feature Engineering

Feature Engineering is the process of turning raw data into relevant information for use in Machine Learning models. A feature, also known as a dimension, is an input variable used in supervised and unsupervised learning to generate model predictions. It's the process of transforming the given data into a form that is easier to interpret.

Importance of Feature Engineering

Feature Engineering is important in machine learning because it helps in making models more accurate as well as improving performance of the model. Data Scientists spend a lot of their time with data, making it important to have accurate models.

When done correctly, the resulting data set is optimal with all important factors affecting the business problem, as such the most accurate predictive models and useful insights are produced.

Feature Engineering Process

The process involves experimentation, model evaluation, and refinement to find the best feature set. It can be broken down into 4 main parts, each with its set of techniques:

Feature Creation: developing new features from existing ones to capture more complex relationships. includes techniques like Scaling and Binning.
Feature Transformation: utilizing mathematical approaches to change feature values to improve performance of machine learning models. Techniques can include normalization.
Feature Extraction: This process reduces complexity and improves visualization. It may result in interpretability loss, and one needs to consider the nature of the data, the problem, and the trade-offs before performing this.
Feature Selection: selecting relevant features from the data to enhance the predictive power and accuracy of the model. Selecting unnecessary or redundant features might result in overfitting, increased computational cost, and decreased model interpretability.

Techniques used in Feature Engineering

here are a few common techniques used in feature engineering, some working better with some algorithms, and some useful in all situations.

Imputation: This is mainly used to handle missing values, that often arise from human error, data flow interruptions, privacy concerns and other factors. There are 2 types of imputations:
- Numerical Imputation: Missing numerical values are generally replaced by the mean of the corresponding value in other records.
- Categorical Imputation: Missing categorical variables are generally replaced by the most commonly occurring value in other records.
Discretization: Also known as Binning, involves taking a set of data values and grouping sets of them together logically into bins (or buckets). It compares each value to the neighborhood of values surrounding it and then sorts data points into a number of bins.
One-hot encoding: categorical data is converted into a form that the machine learning algorithm understands so it can make better predictions. Seen as the inverse of binning, it maps categorical features to binary representations, which are used to map the feature in a matrix.
Scaling: also referred to as normalization, is a standardization technique to rescale features and limit the impact of large scales on models. Involves:
- Min-Max Scaling: This process involves rescaling all values in a feature from 0 to 1. The minimum value in the original range will take 0, the maximum value will take 1, and the rest of the values between the two extremes will be appropriately scaled.
- Z-score scaling: Also referred to as standardization and variance scaling. It rescales features so that they have a shared standard deviation of 1 with a mean of 0.

Feature Engineering Use Case

Below are some examples of where feature engineering is applied:

Obtaining the average and median retweet count of particular tweets.
Extracting pixel information from images.
Car insurance claims predictions.

Conclusion

This article has given a brief overview of what Feature engineering is. It seen as a crucial process in the data industry, especially in data analysis and machine learning, and mastering it is just as important, especially for Data Scientists.

References:

Understanding Feature Engineering in Machine Learning

feature engineering

What is feature engineering?

8 Feature Engineering Techniques for Machine Learning

Feature Engineering for Machine Learning

Understanding Your Data: The Essentials of Exploratory Data Analysis

ekitindi — Sun, 11 Aug 2024 13:44:36 +0000

What is Exploratory Data Analysis

Exploratory Data Analysis, also orefered to as EDA, is a process of analysing data through different steps, methods and using different analysis tools and visuals, to better understand and summarise the data's main characteristics, identify patterns, spot anomalies, test a hypothesis, or check assumptions. It helps summarise the data and discover insights before applying more advanced analysis techniques.

The Importance of Exploratory Data Analysis in Data Science

EDA is a crucial step before you run your data through any algorithm. It helps you detrmine the important variables and those with insignificant impact to the output.

EDA helps data scientists ensure the results produced are valid and applicablel to the desired organisations goals and confirms that the right questions are being asked by stakeholders. It can help anwer questions like standard deviations, categorical variables etc.

Once EDA is performed and insights gained, its features can be used for more complex or sophisticated data analysis, modelling and even machine learning.

Goals of EDA

Put simply, EDA aims to achieve the below:

Understand how data is distributed across different variables in your dataset. This helps identify patterns and potential outliers.
Remove irregularities and unnecessary values from the dataset.
EDA prepares the dataset for further analysis.
Draw meaningful conclusions from the data using statistical techniques.
EDA helps choose the most suitable machine-learning model. It ensures that your model doesn’t suffer from data quality issues due to outliers or anomalies.
EDA contributes to better predictions by machine learning models.

Types of EDA

Univariate Analysis:
Focuses on analysing a single variable at a time, with the main purpose being understanding the variable's distribution, central tendency, and spread.
It uses techniques like:
- Descriptive statistics (Non Graphical) (mean, median, mode, variance, standard deviation).
- Visualizations (Graphical analysis) using histograms, box plots, bar charts, pie charts to visualise the data.
Bivariate Analysis: looks at the relationship between two variables, to understand how one variable is affected or associated to another variable.
It uses mainly graphical techniques like:
- Scatter plots, correlations matrices, line plots and pair plots.
Multivariate Analysis: looks at the relationship between two or more variables in a data set in order to understand the more complex relationships between the variables and interactions within the data.
It uses graphical techniques like:
- grouped bar plots, Multivariate plots (pair plots, parallel coordinates plots), Cluster analysis, heatmaps and correlation matrices.

Tools of Exploratory Data Analysis

The most commonly used tools by data scientists are:

Python: An interpreted, object-oriented programming language with high-level, built-in data structures. It uses various libraries such as:
- Pandas: Provides data structures and functions needed to manipulate structured data seamlessly. Used for data cleaning, manipulation, and summary statistics. Supports large, multi-dimensional arrays and matrices and a collection of mathematical functions.
- Matplotlib: A plotting library that produces static, animated, and interactive visualizations. Used for basic plots like line charts, scatter plots, and bar charts.
- SciPy: Builds on NumPy and provides many higher-level scientific algorithms, used in statistical analysis and additional mathematical functions.
R: An open-source programming language and free software environment for statistical computing and graphics. It has useful libraries like:
- ggplot2: A framework for creating graphics using the principles of the Grammar of Graphics. It is used for Complex and multi-layered visualizations.
- dplyr: A set of tools for data manipulation, offering consistent verbs to address common data manipulation tasks, for use in data wrangling and manipulation.
- tidyr: Provides functions to help you organize your data in a tidy way; for data cleaning and tidying.

Conclusion

Exploratory Data Analysis is a cornerstone for successful data scientists, acting as a guide through the data wilderness, helping them understand the landscape, uncover patterns and hidden gems within data, and pave the way to successful modeling and actionable insights.

EDA is not a one time journey, and you will continuously revisit for new insights. EDA is your compass that you will continuously refer to in your data journey.

Happy Analysing!

References

Becoming an Expert Data Scientist

ekitindi — Wed, 31 Jul 2024 16:45:56 +0000

Introduction

Data Science involves collecting organizing and analyzing raw data from various sources and deriving insights from this data that will help an organization make informed decisions. These decisions aim to improve efficiency, boost profitability and fuel growth.

Why Data Science

If you find yourself to be curious, with an analytical mind, then Data Acsience could be for you.

Data Science is one of the fastest-growing industries around the world, with the U.S. Bureau of Labor Statistics (BLS) estimating a 22% growth in Data Science job creation through 2030. The salary ranges too, are very attractive, with an average of $103,903, or $8,659 per month, according to BLS. Nearly every industry and organisation, cutting across governments, retail, and healthcare, requires data scientists.

Data Science Life Cycle and Skills Required

As much as every data science project is unique, depending on the problem and industry, most will follow a similar life cycle.
There are typically 6 steps in the data science life cycle are as below:

Problem Understanding

Also referred to as business understanding, involves understanding the organisation, defining a problem identified by the organisation, objectives to be achieved and constraints.

Skills: Strong business acumen, communication, and problem-solving abilities.
Technologies: The focus here remains on understanding the business objectives.

Data Collection and Exploration

Gather relevant data from various sources, such as databases, Excel files, text files, APIs, web scraping, or even real-time data streams. The type and volume of data collected largely depend on the problem you’re addressing. The data is then stored appropriately for further processing.

Skills: Data querying (e.g. SQL), data cleaning, and exploratory data analysis (e.g. EDA).
Technologies: SQL, Python (pandas, NumPy), R.

Data Cleaning and Preprocessing

This is most often the most time-consuming step of the life cycle. It involves cleaning messy data, handling missing values, transforming features and preparing data for modelling. The objective is to create a clean, high-quality dataset that will yield accurate and reliable analytical results.

Skills: Data wrangling, feature engineering, and statistical knowledge.
Technologies: Python (pandas, scikit-learn), R.

Data Analysis and Modeling

You explore the prepared data to understand its patterns, characteristics, and potential anomalies. Apply machine learning algorithms (regression, classification, clustering) to build predictive models.

Skills: Statistical analysis, machine learning, model selection.
Technologies: Python (scikit-learn, TensorFlow, PyTorch), R.

Deployment and Maintenance

This involves interpreting and communicating the results derived from the data analysis. You will communicate your findings to stakeholders effectively, using clear, concise language and compelling visuals using tools like Tableau or PowerBI. The goal is to convey these findings to non-technical stakeholders in a way that influences decision-making or drives strategic initiatives. If satisfactory you will deploy the models into production, monitor performance and update as needed.

Skills: Software engineering, cloud deployment, monitoring, communication (storytelling).
Technologies: Docker, Kubernetes, cloud platforms (e.g. AWS, Azure, GCP.

Point to note:

You may have to go back in the cycle, at any step, before completion if you find that the models do not perform as expected. That is why the data cleaning phase consumes a lot of time, over 50%, and is considered one of the most important steps in the cycle.

Career Path for a Data Scientist

Apart from being curious and analytical, there are a range of skills that you need to develop to progress in this field.
There are various levels one will go through as a Data Scientist, as you gather experience and skills along the way, each applicable in the Data Science life cycle.

Junior Data Scientist: You will be working mainly on the basics of data analysis, like extracting, cleaning, integrating and loading data. They will often use pre-existing models, and work within specifications set by senior Data Scientists. Typically you will get about 2-3 years experience at this level.
Mid-Level Data Scientist: You will perform more exploratory analysis, and build statistical models to solve problems. You may also work with senior data scientists in machine learning and AI.
Senior Data Scientist: You may be here after 3 to 7 years. You will be putting models to work in conjunction with other advanced tools. You will monitor and fine-tune the organisation's methodologies, and collaborate with other organisational stakeholders in communicating the relevant insights.
Data Science Managers: Here you typically have at least 5 years prior experience as a Data Scientist, with about 2-3 years in a supervisory role. Yours is to hire the right people, set realistic goals and KPIs, create a productive environment and ensure your organisation remains competitive by being aware of changing developments within the industry.

Across various industries, the job growth for data scientists is on the rise. With the increasing availability and affordability of the Internet and the growth of the "Internet of Things", the need for data scientists will continue to grow.

Consider the below examples of industries where Data Science is applicable:

Finance: Fraud detection and mitigating risks.
Health: Health applications, disease tracking and management.
Transport: Managing traffic and Autonomous Vehicle (AV) development.

Begin Your Journey as a Data Scientist

Start Learning Key Data Science concepts and tools

You will need to start to understand the fundamental concepts of data science. Get familiar with statistics and mathematics.
You need to learn to code as an essential skill as well. SQL, Python and R are the most commonly used.
Get familiar with machine learning as well. Finally, you need to be familiar with visualisation techniques and tools like Tableau or PowerBI.

Develop a Portfolio

Hands-on experience is crucial in this field. You can start working on small personal projects and documenting them in a portfolio. This helps improve your skills. A strong portfolio demonstrates your creativity and practical skills.

Lean On your Networks

Connect with professionals in the field through social media, meetup events/hackathons and communities, and learn from each other. Many jobs have come as a result of these through referrals.
Get yourself a mentor coach, who can offer guidance, give you insights on the industry and help you develop a resume.

Stay Curious And Keep Learning

Curiosity is one of the essential soft skills you will need as well. You will also need to keep abreast with industry standards and developments, and tools in use.

Conclusion

Data continues to be deeply embedded in our lives, and we are experiencing exponential growth in the availability of big data.
Data Science is at the forefront of giving meaningful insights for data-driven decision-making across various industries. Now is a good time as any to start your journey in the world of data as a Data Scientist where many opportunities await you.