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Data Science

Statistics, Python, pandas and machine learning projects.

  • 1 month (4 weeks)
  • 20 topics
  • Final exam
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Data Science

Your learning path

4 weeks from start to certificate

  1. 1

    Week 1

    Data science foundations

  2. 2

    Week 2

    Statistics & visualisation

  3. 3

    Week 3

    Modelling

  4. 4

    Week 4

    Capstone

  5. Finish

    Exam & certificate

Every week

How each week works

Learn

Live class with your trainer

Practice

Guided hands-on exercises

Build

Mini project for your portfolio

Review

Feedback and Q&A

Concept lab

See how the core concepts connect

Follow the path from foundations to real workplace application. Every concept feeds the next project.

1

Week 1

Introduction to Data Science

2

Week 1

Python for Data Science (Part 1)

3

Week 2

Descriptive Statistics

4

Week 2

Inferential Statistics Basics

5

Week 3

Introduction to Machine Learning

6

Week 3

Regression Models

7

Week 4

Advanced Classification Models

8

Week 4

Unsupervised Learning & Clustering

Topics per week

20 topics · 80 lessons
Week 15
Week 25
Week 35
Week 45

Course curriculum

1

Week 1 · 5 topics · 20 lessons

Data science foundations

By the end of this week: Learners will establish a strong understanding of data science lifecycle, essential programming skills, and data manipulation techniques.

  1. 1.1Introduction to Data Science
    • What is data science?
    • Data Science vs. AI/ML
    • Data Science project lifecycle
    • Ethical considerations in Data Science
  2. 1.2Python for Data Science (Part 1)
    • Python environment setup (Anaconda)
    • Basic Python syntax, data types
    • Control flow, functions
    • Libraries: NumPy fundamentals
  3. 1.3Data Manipulation with Pandas
    • Pandas DataFrames and Series
    • Loading and saving data (CSV, Excel)
    • Data selection and filtering
    • Missing data handling (dropna, fillna)
  4. 1.4Data Cleaning and Preprocessing
    • Identifying data quality issues
    • Handling duplicates and outliers
    • Data type conversions
    • Feature scaling techniques
  5. 1.5SQL for Data Analysts
    • Relational database concepts
    • SELECT, FROM, WHERE clauses
    • JOIN operations (INNER, LEFT, RIGHT)
    • Aggregation functions (COUNT, SUM, AVG)
Week 1
Data science foundations
Introduction to Data Science
Python for Data Science (Part 1)
Data Manipulation with Pandas
Data Cleaning and Preprocessing
SQL for Data Analysts

Hands-on project

Data Cleaning & Exploration with Pandas

2

Week 2 · 5 topics · 20 lessons

Statistics & visualisation

By the end of this week: Learners will apply statistical concepts to data, create insightful visualizations, and present findings effectively.

  1. 2.1Descriptive Statistics
    • Measures of central tendency (mean, median)
    • Measures of dispersion (variance, std dev)
    • Percentiles and quartiles
    • Data distribution shapes
  2. 2.2Inferential Statistics Basics
    • Sampling and estimation
    • Hypothesis testing concepts
    • P-values and significance levels
    • T-tests and Chi-squared tests
  3. 2.3Data Visualization with Matplotlib
    • Matplotlib basics (figures, axes)
    • Line plots, scatter plots, bar charts
    • Histograms and box plots
    • Customizing plots (labels, titles, legends)
  4. 2.4Advanced Visualization with Seaborn
    • Seaborn vs. Matplotlib
    • Relational plots (scatterplot, lineplot)
    • Categorical plots (boxplot, violinplot)
    • Distribution plots (distplot, jointplot)
  5. 2.5Storytelling with Data
    • Principles of effective visualization
    • Choosing the right chart type
    • Dashboard design concepts
    • Presenting data insights
Week 2
Statistics & visualisation
Descriptive Statistics
Inferential Statistics Basics
Data Visualization with Matplotlib
Advanced Visualization with Seaborn
Storytelling with Data

Hands-on project

Exploratory Data Analysis Report

3

Week 3 · 5 topics · 20 lessons

Modelling

By the end of this week: Learners will build, evaluate, and interpret various machine learning models for prediction and classification tasks.

  1. 3.1Introduction to Machine Learning
    • Supervised vs. Unsupervised Learning
    • Regression vs. Classification problems
    • Bias-Variance Trade-off
    • Model training and testing workflows
  2. 3.2Regression Models
    • Linear Regression (sklearn)
    • Polynomial Regression
    • Regularization (Lasso, Ridge)
    • Evaluating regression models (RMSE, R²)
  3. 3.3Classification Models (Part 1)
    • Logistic Regression
    • K-Nearest Neighbors (KNN)
    • Support Vector Machines (SVC)
    • Decision Trees classifier
  4. 3.4Model Evaluation and Selection
    • Confusion Matrix interpretation
    • Accuracy, Precision, Recall, F1-Score
    • ROC curves and AUC score
    • Cross-validation techniques
  5. 3.5Feature Engineering and Selection
    • Creating new features
    • Encoding categorical variables (OneHotEncoder)
    • Feature importance analysis
    • Dimensionality reduction (PCA basics)
Week 3
Modelling
Introduction to Machine Learning
Regression Models
Classification Models (Part 1)
Model Evaluation and Selection
Feature Engineering and Selection

Hands-on project

Predictive Model Development

4

Week 4 · 5 topics · 20 lessons

Capstone

By the end of this week: Learners will integrate all acquired skills to solve a real-world data science problem, present their solution, and understand deployment basics.

  1. 4.1Advanced Classification Models
    • Ensemble methods (Random Forest)
    • Gradient Boosting (XGBoost, LightGBM)
    • Hyperparameter tuning (GridSearchCV)
    • Model interpretability (SHAP, LIME)
  2. 4.2Unsupervised Learning & Clustering
    • K-Means Clustering algorithm
    • Hierarchical Clustering
    • DBSCAN for density-based clustering
    • Evaluating clustering results
  3. 4.3Text Data (NLP Basics)
    • Text data preprocessing (tokenization)
    • Bag-of-Words, TF-IDF vectors
    • Sentiment analysis concepts
    • Simple text classification
  4. 4.4Data Science Project Workflow
    • Problem definition and scope
    • Data acquisition and understanding
    • Model deployment strategies (Flask API)
    • Monitoring and maintenance
  5. 4.5Career & Further Learning
    • Building a data science portfolio
    • Interview preparation tips
    • Continuous learning resources
    • Current industry trends
Week 4
Capstone
Advanced Classification Models
Unsupervised Learning & Clustering
Text Data (NLP Basics)
Data Science Project Workflow
Career & Further Learning

Hands-on project

End-to-End Data Science Project

Final exam & certificate

Finish the 4 weeks, then take the online exam. Score 70% or more to pass.

Job-ready syllabus

Learn it. Build it. Explain it. Use it.

The syllabus goes beyond watching lessons. You practice the tasks employers expect, produce evidence of your skills, and prepare to discuss your work clearly.

Workplace skills

Apply Introduction to Data Science, Python for Data Science (Part 1), Data Manipulation with Pandas, Data Cleaning and Preprocessing in guided business scenarios.

Portfolio proof

Complete 4 practical projects, including Data Cleaning & Exploration with Pandas.

Professional practice

Present your work, respond to feedback, document decisions, and improve the final result.

Interview readiness

Review common role questions and explain your process, tools, trade-offs, and project outcomes.

Your completion pack

Weekly projects, trainer feedback, final assessment, and a certificate you can add to your résumé and LinkedIn profile.

Why learn from us?

Six reasons students pick M - IT Solutions

One-month programs built around live teaching, real projects and support that actually shows up.

Live online classes

Learn with a real instructor in real time — not pre-recorded videos you watch alone.

Hands-on projects

Every week ends with practical work you can show employers on day one.

1-on-1 trainer time

Stuck or want to go deeper? Book private sessions with a trainer whenever you need.

A real learning area

Lessons, progress tracking and exams all live in your account, on any device.

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We train individuals and whole teams — from small businesses to enterprise programs.

Verified & supported

Verified accounts, real certificates and a support team that answers when you write.

1000+ learners trainedLive, instructor-ledProject-based every week
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