Data Science
Data Science Course Online
Python, Machine Learning & AI
Dive into data science with practical online training. Master Python, ML, and AI through rigorous hands-on project work. You will receive mentor-led guidance, mock interview preparation, and develop in-demand career-focused skills.
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Course Duration: Self-paced
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Skill Level: Beginner
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Training Language: English
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Learning Mode: Online
Mentor-Led, Hands-On Learning
Become a job-ready Data Scientist. This master program builds your foundations in Python, SQL/R, data wrangling and statistics, then advances through machine learning, deep learning, NLP, and BI dashboards—with professional tooling and a real capstone. Core stack: Python, R, SQL...
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Hands-On Projects
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Mentor-Led Support
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Tekizz Academy Course Completion Certificate
After successful completion, you will receive a Tekizz Academy digital certificate that supports your professional profile, validates your course participation, and highlights your practical learning progress.
- check_circle Shareable certificate for LinkedIn and professional profiles
- check_circle Useful for resume and LinkedIn profile updates
- check_circle Access to Tekizz Academy learner support community
Course Curriculum & Practical Modules
A structured learning roadmap designed to build practical skills through step-by-step lessons, hands-on exercises, real-world examples, and career-focused training.
WEEK 01
Introduction to Data Science
- play_circle Overview of Data Science
- play_circle Roles and Responsibilities
- play_circle Importance in Modern Business
- + 1 more lessons
WEEK 02
Methodologies: SDLC, Agile & Waterfall
- play_circle Software Development Life Cycle (SDLC)
- play_circle Phases of SDLC
- play_circle Principles of Agile
- + 3 more lessons
WEEK 03
Programming for Data Engineering
- play_circle Python Basics: Syntax & Structures
- play_circle Python: Data Types and Functions
- play_circle SQL Basics: Writing Queries
- + 3 more lessons
WEEK 04
IDEs & Developer Setup
- play_circle VS Code: Setup & Extensions
- play_circle VS Code: Debugging
- play_circle PyCharm: Setup & Usage
- + 1 more lessons
WEEK 05
Data Manipulation & Visualization (Py Data Stack)
- play_circle NumPy: Array Operations
- play_circle Pandas: DataFrame Manipulation
- play_circle Matplotlib: Core Plots
- + 1 more lessons
WEEK 06
SciPy, scikit-learn & ggplot2
- play_circle SciPy: Scientific Computing
- play_circle scikit-learn: ML Workflows
- play_circle R ggplot2: Creating Visualizations
WEEK 07
Machine Learning I: Regression & Trees
- play_circle Linear Regression: Concepts & Applications
- play_circle Logistic Regression: Concepts & Applications
- play_circle Model Building & Evaluation
- + 2 more lessons
WEEK 08
Supervised vs Unsupervised & Classification
- play_circle Supervised vs Unsupervised: Key Concepts
- play_circle Classification Algorithms: Techniques
- play_circle Building & Evaluating Classifiers
WEEK 09
Advanced ML I: SVM & Random Forests
- play_circle SVM: Concepts & Applications
- play_circle SVM: Model Building & Evaluation
- play_circle Random Forests: Concepts & Applications
- + 1 more lessons
WEEK 10
Advanced ML II: Naive Bayes, K-Means, KNN
- play_circle Naive Bayes: Concepts & Applications
- play_circle Naive Bayes: Model Building & Evaluation
- play_circle K-Means Clustering: Concepts & Applications
- + 3 more lessons
WEEK 11
Deep Learning I: Overview & ANN
- play_circle Intro to Deep Learning & ML vs DL
- play_circle Neural Networks (ANN): Concepts & Architectures
- play_circle ANN: Building & Training
WEEK 12
Deep Learning II: CNN & RNN
- play_circle CNN: Concepts & Applications
- play_circle CNN: Building & Training
- play_circle RNN: Concepts & Applications
- + 1 more lessons
WEEK 13
DL Architectures: LSTM & Encoder-Decoder
- play_circle LSTM: Concepts & Applications
- play_circle LSTM: Building & Training
- play_circle Encoder-Decoder: Concepts & Applications
- + 1 more lessons
WEEK 14
NLP & Transformers (BERT)
- play_circle Introduction to NLP
- play_circle BERT: Concepts & Applications
- play_circle Fine-Tuning BERT for NLP Tasks
WEEK 15
Business Intelligence: Tableau & Power BI
- play_circle Tableau: Connect & Build Dashboards
- play_circle Power BI: Reports & Dashboards
- play_circle Data Visualization Best Practices
WEEK 16
Databases: SQL Server & MySQL
- play_circle SQL Server: Setup & Management
- play_circle SQL Server: Advanced SQL
- play_circle MySQL: Setup & Management
- + 1 more lessons
WEEK 17
Version Control & Collaboration
- play_circle Git Basics: Commands & Concepts
- play_circle Branching and Merging
- play_circle GitHub: Collaboration & Pull Requests
WEEK 18
Data Analysis & Reporting
- play_circle Excel for Data Analysis
- play_circle Advanced Excel Techniques
- play_circle Creating Effective Reports
- + 1 more lessons
WEEK 19
Project Management & Soft Skills
- play_circle Using MS Office for Documentation
- play_circle Project Planning & Execution
- play_circle Communication & Collaboration
- + 1 more lessons
WEEK 20
Capstone Project
- play_circle Defining Project Scope & Objectives
- play_circle Data Collection, Cleaning & Analysis
- play_circle Model Building & Evaluation
- + 1 more lessons
Secure Your Course Enrollment
Join this Tekizz Academy online course and build practical, career-focused technology skills with flexible learning support.
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Quick Course Access
Access course materials and learning resources after enrollment.
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Mentor-Led Guidance
Get structured guidance, doubt-clearing support, and career-focused learning help.
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Course Frequently Asked Questions
Who is this course for? expand_more
Aspiring or upskilling professionals who want a practical, tool-driven path to Data Science roles.
Which programming languages are covered? expand_more
Python for primary implementation, with R basics and an introduction to SAS.
What machine learning topics will I learn? expand_more
Regression (linear/logistic), Decision Trees, Classification workflows, SVM, Random Forest, Naive Bayes, KNN, and K-Means clustering.
Do you cover deep learning? expand_more
Yes—ANN, CNN, RNN/LSTM, and encoder–decoder architectures.
Is NLP included? expand_more
Yes—introductory NLP concepts and fine-tuning BERT for common tasks.
Which data and visualization tools are used? expand_more
NumPy, Pandas, SciPy, Scikit-learn, Matplotlib, Seaborn, and ggplot2 in R.
What databases and SQL are taught? expand_more
SQL Server and MySQL, with advanced querying and data operations.
Do you include BI and reporting? expand_more
Yes—Tableau and Power BI dashboards, plus Excel for analysis and reporting.
How do we collaborate and version control? expand_more
Git/GitHub basics, branching, pull requests, and repository management.
Is there mentorship and interview prep? expand_more
Yes—1:1 personalized mentorship and mock interview preparation.