This 6-month certification course is designed to equip individuals with the essential skills and knowledge required to become proficient data analysts. The program covers a comprehensive range of topics, from foundational statistical concepts and programming to advanced data visualization, machine learning basics, and real-world project applications.

Program Overview:

Duration: 6 Months

Format: Online / Offline (Hybrid Mode) (Self-paced with live sessions and mentorship)

Target Audience: Beginners with a strong analytical aptitude, professionals looking to transition into data analytics, and anyone interested in building a career in data.

Classes: 2 Classes / Week

Class Duration: 2 Hours / Class

Month 1: Foundations of Data Analytics & Statistical Concepts

Duration: 4 Weeks

Module Objectives:

• Understand the role of data analytics in various industries.

• Differentiate between different types of data and analytical approaches.

• Master fundamental statistical concepts essential for data analysis.

• Learn to summarize, describe, and interpret data using descriptive statistics.

Key Topics:

• Week 1: Introduction to Data Analytics

  • What is Data Analytics? Role and Responsibilities of a Data Analyst

  • Types of Data (Quantitative, Qualitative, Structured, Unstructured)

  • Data Analytics Lifecycle (Problem Definition, Data Collection, Cleaning, Analysis, Interpretation, Reporting)

  • Tools Overview: Excel, SQL, Python/R, Tableau/Power BI

• Week 2: Descriptive Statistics

  • Measures of Central Tendency (Mean, Median, Mode)

  • Measures of Dispersion (Variance, Standard Deviation, Range, IQR)

  • Frequency Distributions and Histograms

  • Skewness and Kurtosis

• Week 3: Probability and Inferential Statistics Basics

  • Basic Probability Concepts

  • Probability Distributions (Normal, Binomial, Poisson)

  • Sampling Techniques and Central Limit Theorem

  • Introduction to Hypothesis Testing

• Week 4: Data Gathering and Preparation with Excel

  • Importing Data into Excel

  • Basic Data Cleaning in Excel (Removing Duplicates, Handling Missing Values)

  • Using Excel Functions for Data Analysis (SUMIF, COUNTIF, VLOOKUP, INDEX-MATCH)

  • Pivot Tables and Pivot Charts for Data Summarization

Tools/Technologies: Excel, Basic understanding of statistical software concepts. Projects/Assignments: Data summarization reports using Excel, basic statistical analysis of small datasets.

Month 2: Programming for Data Analytics – Python Fundamentals

Duration: 4 Weeks

Module Objectives:

• Develop foundational programming skills in Python.

• Learn to manipulate and analyze data using Python libraries.

• Perform data loading, cleaning, and basic transformations.

Key Topics:

• Week 5: Python Basics for Data Analysis

  • Introduction to Python: Variables, Data Types, Operators

  • Control Flow (If/Else, Loops), Functions

  • Introduction to Jupyter Notebooks/Google Colab

• Week 6: NumPy for Numerical Computing

  • Introduction to NumPy Arrays

  • Array Operations, Indexing, Slicing

  • Mathematical and Statistical Operations with NumPy

• Week 7: Pandas for Data Manipulation (Part 1)

  • Introduction to Pandas DataFrames and Series

  • Loading Data (CSV, Excel)

  • Selecting and Filtering Data

  • Handling Missing Values (fillna, dropna)

• Week 8: Pandas for Data Manipulation (Part 2)

  • Data Cleaning Techniques (Type Conversion, Removing Duplicates, String Operations)

  • Grouping and Aggregating Data (groupby)

  • Merging and Joining DataFrames

Tools/Technologies: Python, Jupyter Notebooks/Google Colab, Pandas, NumPy. Projects/Assignments: Data cleaning and transformation scripts using Pandas, basic data aggregation tasks.

Month 3: Data Storage, Retrieval & SQL

Duration: 4 Weeks

Module Objectives:

• Understand relational databases and SQL.

• Write complex SQL queries for data extraction and manipulation.

• Gain an introduction to NoSQL databases.

Key Topics:

• Week 9: Relational Databases and SQL Fundamentals

  • Introduction to Databases (DBMS, RDBMS)

  • Database Schema, Tables, Columns, Rows

  • Introduction to SQL: SELECT, FROM, WHERE, ORDER BY

• Week 10: Advanced SQL Queries

  • JOIN Operations (INNER, LEFT, RIGHT, FULL OUTER)

  • Aggregate Functions (COUNT, SUM, AVG, MIN, MAX)

  • GROUP BY, HAVING Clauses

  • Subqueries

• Week 11: Data Manipulation Language (DML) & Data Definition Language (DDL)

  • INSERT, UPDATE, DELETE Statements

  • CREATE TABLE, ALTER TABLE, DROP TABLE

  • Constraints (PRIMARY KEY, FOREIGN KEY, NOT NULL, UNIQUE)

• Week 12: Introduction to NoSQL Databases & Cloud Databases

  • Overview of NoSQL Concepts (Key-Value, Document, Column-Family, Graph)

  • When to use NoSQL vs. SQL

  • Brief introduction to cloud databases (e.g., AWS S3 for data storage, Google BigQuery concepts)

Tools/Technologies: MySQL/PostgreSQL (or SQLite), DB Browser for SQLite. Projects/Assignments: Design a simple database schema, write complex SQL queries to answer business questions.

Month 4: Exploratory Data Analysis (EDA) & Data Visualization

Duration: 4 Weeks

Module Objectives:

• Perform comprehensive exploratory data analysis.

• Choose appropriate visualization types for different data.

• Create compelling and insightful data visualizations using Python and dedicated BI tools.

Key Topics:

• Week 13: Principles of EDA

  • Objectives and Techniques of EDA

  • Univariate, Bivariate, and Multivariate Analysis

  • Identifying Outliers and Anomalies

  • Feature Engineering Basics

• Week 14: Data Visualization with Matplotlib & Seaborn (Python)

  • Introduction to Matplotlib: Basic Plots (Line, Bar, Scatter, Histogram)

  • Customizing Plots: Titles, Labels, Legends, Colors

  • Introduction to Seaborn: Statistical Data Visualization

  • Creating advanced plots (Box plots, Violin plots, Heatmaps, Pair plots)

• Week 15: Introduction to Business Intelligence Tools – Tableau/Power BI (Part 1)

  • Overview of Tableau/Power BI Interface and Workspace

  • Connecting to Data Sources

  • Creating Basic Charts (Bar, Line, Pie, Scatter)

  • Building Dashboards

• Week 16: Business Intelligence Tools – Tableau/Power BI (Part 2)

  • Calculated Fields and Parameters

  • Filters, Sets, and Groups

  • Advanced Dashboard Design and Interactivity

  • Storytelling with Data

Tools/Technologies: Python (Matplotlib, Seaborn), Tableau Public/Power BI Desktop. Projects/Assignments: Conduct EDA on a real-world dataset and create a compelling dashboard using Tableau/Power BI.

Month 5: Advanced Analytics & Machine Learning Basics

Duration: 4 Weeks

Module Objectives:

• Apply advanced statistical techniques.

• Understand the fundamentals of machine learning.

• Implement basic supervised and unsupervised learning algorithms.

Key Topics:

• Week 17: Advanced Statistical Hypothesis Testing

  • T-tests (One-sample, Independent, Paired)

  • ANOVA (Analysis of Variance)

  • Chi-Square Test

  • Correlation and Covariance

• Week 18: Introduction to Machine Learning

  • What is Machine Learning? Supervised vs. Unsupervised Learning

  • Training, Validation, and Test Sets

  • Overfitting and Underfitting

  • Model Evaluation Metrics (Accuracy, Precision, Recall, F1-Score, RMSE)

• Week 19: Supervised Learning – Regression

  • Linear Regression (Simple and Multiple)

  • Assumptions of Linear Regression

  • Model Training and Prediction with Scikit-learn

• Week 20: Supervised Learning – Classification

  • Logistic Regression

  • Decision Trees and Random Forests (Introduction)

  • Confusion Matrix and ROC Curve

Tools/Technologies: Python (Scikit-learn, SciPy). Projects/Assignments: Implement a linear regression model, perform a basic classification task on a dataset.

Month 6: Business Applications, Reporting & Capstone Project

Duration: 4 Weeks

Module Objectives:

• Apply data analytics skills to solve real-world business problems.

• Develop strong communication and storytelling skills for presenting insights.

• Complete a comprehensive capstone project demonstrating acquired knowledge.

Key Topics:

• Week 21: Data-Driven Decision Making & Business Acumen

  • Translating Business Problems into Analytical Questions

  • Key Performance Indicators (KPIs) and Metrics

  • A/B Testing Concepts

  • Ethics in Data Analytics

• Week 22: Effective Communication & Storytelling with Data

  • Structuring Data Presentations

  • Crafting a Narrative from Data Insights

  • Tips for Presenting Complex Information to Non-Technical Audiences

  • Report Writing and Dashboard Best Practices

• Week 23-24: Capstone Project

  • Students will work on a real-world dataset (provided or self-selected/approved) from start to finish.

  • This includes:

    • Problem definition and hypothesis formulation

    • Data collection/acquisition

    • Data cleaning and preprocessing

    • Exploratory Data Analysis

    • Statistical analysis or basic machine learning model building

    • Visualization and dashboard creation

    • Interpretation of results and actionable recommendations

    • Final presentation and report

Tools/Technologies: All previously covered tools (Python, SQL, Tableau/Power BI, Excel). Projects/Assignments: Comprehensive Capstone Project with a written report and presentation.

Assessment:

• Weekly quizzes and assignments

• Mid-term examination

• Module-specific projects

• Final Capstone Project and Presentation

Prerequisites:

• Basic computer literacy

• No prior programming or statistics knowledge required, but a keen interest in data and problem-solving is highly recommended.

Upon successful completion of this program, participants will receive a Data Analytics Certification, demonstrating their proficiency in the field and readiness for entry-level to mid-level data analyst roles.