This course provides a solid foundation in statistics, focusing on the essential concepts and methods used in data analysis. It is designed to help learners build the skills necessary to understand, interpret, and apply statistical techniques in real-world situations, especially in data science, research, and decision-making.

1. Descriptive Statistics

This section introduces the tools used to summarize and describe data.

• Measure of Central Tendency (Mean, Median, Mode): Learn how to identify the center point of a dataset and understand when to use each measure effectively.
• Variability (Standard Deviation, Variance): Understand how data values are spread around the mean and why variability is critical in comparing datasets.
• Graphical Summaries (Bar Plots, Histograms): Explore visual tools to represent data distribution and patterns for easy interpretation.

2. Inferential Statistics

This section covers methods that allow making predictions or generalizations about a population based on sample data.

• Probability Distribution: Understand the basics of probability and key distributions (normal, binomial, Poisson) that form the foundation of statistical inference.
• Hypothesis Testing: Learn how to test assumptions using statistical methods (t-tests, chi-square tests, etc.), interpret p-values, and draw meaningful conclusions.
• Sampling: Discover different sampling techniques, their importance, and how they impact the accuracy of results.
• Experiment Design: Understand how to design experiments and surveys in a structured way to reduce bias and ensure reliable results.

After completing this module, you will be able to achieve these certifications

• Summarize datasets using numerical and graphical methods.
• Apply measures of central tendency and variability to analyze data.
• Understand probability distributions and their applications.
• Conduct hypothesis testing and interpret results confidently.
• Use appropriate sampling methods for data collection.
• Design simple experiments and analyze outcomes effectively.

This course introduces learners to Python programming with a strong focus on its applications in Machine Learning (ML) and Data Science. The course covers Python basics, essential libraries, and practical techniques for preparing data before feeding it into ML models. Learners will gain both theoretical understanding and hands-on coding practice.

1. Introduction

• Basics of Python programming: variables, data types, operators, and control structures.
• Functions, loops, and conditionals for solving problems.
• Introduction to Jupyter Notebook / Google Colab for data science projects.
• Writing clean, reusable, and efficient Python code.

2. Python Libraries for Machine Learning

• NumPy: Working with arrays, mathematical operations, linear algebra.
• Pandas: Handling tabular data, DataFrames, data cleaning, filtering, and transformation.
• Scikit-Learn: Overview of machine learning workflows, preprocessing tools, and basic algorithms.
• Hands-on exercises with small datasets to practice these libraries.

3. Handling Missing Data

• Understanding missing data and its impact on analysis.
• Techniques for handling missing values:

     1. Deletion methods (row/column removal).

     2. Imputation methods (mean, median, mode,  forward/backward fill).

• Practical implementation in Pandas and Scikit-Learn.

4. Encoding Categorical Variables

• Why categorical variables need to be encoded in ML.
• Label Encoding vs. One-Hot Encoding.
• Using Scikit-Learn and Pandas to perform encoding.
• Real-life dataset examples (e.g., encoding gender, country, categories).

5. Dataset Split & Feature Scaling Practice

• Splitting datasets into training and testing sets.
• Importance of data splitting for unbiased evaluation.
• Feature scaling techniques:

         o Standardization (Z-score normalization).

         o Min-Max scaling.

• Implementing scaling with Scikit-Learn (StandardScaler, MinMaxScaler).
• Practical exercise: Prepare a dataset from raw data to model-ready features.

After completing this module, you will be able to achieve these certifications

• Gain confidence in Python programming for ML.
• Use NumPy, Pandas, and Scikit-Learn effectively.
• Clean and preprocess datasets by handling missing and categorical data.
• Prepare datasets with splitting and feature scaling for ML models.
• Be ready to move forward to machine learning algorithms and modeling.

This course is designed to give learners a strong foundation in Structured Query Language (SQL), which is essential for working with databases, extracting insights, and supporting data-driven decision-making. The course moves from basics to advanced concepts, preparing students for both academic and professional use cases in data analysis, business intelligence, and machine learning pipelines.

1. Introduction

• Overview of databases and the role of SQL.
• Difference between relational and non-relational databases.
• Introduction to tables, rows, and columns.
• Writing basic SQL queries: SELECT, FROM, WHERE, ORDER BY.
• Hands-on practice with sample datasets.

2. Inner, Outer, Left, Right Joins

• Understanding the purpose of joins in relational databases.
• Inner Join – retrieving matching records from two tables.
• Left Join – returning all rows from the left table with matches from the right.
• Right Join – returning all rows from the right table with matches from the left.
• Full Outer Join – retrieving all records when there is a match in either table.
• Practical exercises with multiple related tables.

3. Aggregate Functions

• Introduction to aggregate queries for summarizing data.
• Common functions: COUNT(), SUM(), AVG(), MIN(), MAX().
• Using GROUP BY and HAVING clauses to group and filter results.
• Real-life examples: calculating total sales, average scores, or customer counts.

4. Subqueries

• What are subqueries and why they are used.
• Writing subqueries in SELECT, FROM, and WHERE clauses.
• Nested queries for filtering and advanced calculations.
• Performance considerations when using subqueries.
• Examples: finding employees with above-average salary, or customers with highest purchases.

• Introduction to Window Functions: analyzing rows relative to current row.
• Common functions:

           o RANK() / DENSE_RANK()           

           o LEAD() / LAG()

• Analytical use cases: running totals, moving averages, trend analysis.
• Combining window functions with partitions and ordering for deep insights.

After completing this module, you will be able to achieve these certifications.

• Understand how to query and manipulate relational databases using SQL.
• Apply joins to combine multiple tables for meaningful insights.
• Summarize and analyze data using aggregate functions.
• Write and optimize subqueries for advanced data retrieval.
• Use window and analytical functions to perform complex analysis.
• Be prepared to apply SQL in data analytics, business intelligence, and machine learning workflows.

1. Microsoft Excel

Data Entry and Cleaning

• Importing datasets into Excel.
• Using filters, conditional formatting, and data validation.
• Cleaning messy datasets with text functions and logical operations.

Formulas and Functions

• Essential formulas: SUM, IF, VLOOKUP, HLOOKUP, INDEX, MATCH.
• Statistical functions: AVERAGE, MEDIAN, STDEV, etc.
• Logical and nested formulas for complex problem-solving.

Data Visualization

• Creating bar charts, line charts, pie charts, and combo charts.
• Using pivot tables and pivot charts for summarizing large datasets.
• Dashboard creation for interactive reporting.

Data Analysis Tools

• What-If Analysis (Goal Seek, Scenario Manager, Data Tables).
• Solver for optimization problems.
• Trend analysis and forecasting.

2. Microsoft Power BI

Introduction to Power BI

• Understanding the Power BI interface and workflow.
• Connecting to multiple data sources (Excel, SQL, APIs, etc.).

Data Transformation

• Cleaning and shaping data with Power Query.
• Handling missing values and transforming columns.
• Creating relationships between multiple tables.

Data Modeling

• Introduction to DAX (Data Analysis Expressions).
• Creating calculated columns, measures, and KPIs.
• Building star schemas and data models for performance optimization.

Data Visualization

• Designing reports with charts, maps, and KPIs.
• Using slicers and filters for interactivity.
• Best practices for designing professional dashboards.

Publishing and Sharing

• Publishing reports to the Power BI Service.
• Setting up scheduled data refresh.
• Sharing dashboards securely within an organization.

After completing this module, you will be able to achieve these certifications

• By completing this course, learners will:
• Build strong data analysis and reporting skills with Microsoft Excel.
• Create pivot tables, charts, and dashboards for summarizing large datasets.
• Use advanced Excel tools for scenario analysis and forecasting.
• Design, build, and share interactive dashboards in Power BI.
• Connect and transform multiple data sources into meaningful insights.
• Be ready to apply these tools in business, research, and data science projects.

This course introduces learners to the principles, methods, and applications of data mining, an essential field in data science that focuses on discovering hidden patterns, extracting meaningful information, and making predictions from large datasets. The course covers data collection, preprocessing, pattern discovery, and real-world applications through hands-on practice and projects.

1. Introduction

• Definition and importance of data mining.
• Applications in business, healthcare, finance, e-commerce, and social media.
• Differences between data mining, machine learning, and statistics.
• The Knowledge Discovery in Databases (KDD) process.

2. Data Preprocessing & Cleaning

• Handling missing, inconsistent, and noisy data.
• Normalization, transformation, and standardization techniques.
• Feature selection and dimensionality reduction.
• Tools and libraries: Python (Pandas, NumPy, Scikit-Learn).

3. Web Scraping & Data Collection

• Methods of collecting structured and unstructured data.
• Web scraping techniques with BeautifulSoup, Scrapy, and Selenium.
• Working with APIs for real-time data collection.
• Ethical considerations and legal aspects of web data mining.

4. Association Rule Mining

• Introduction to market basket analysis.
• Concepts: Support, Confidence, and Lift.
• Apriori and FP-Growth algorithms.
• Real-life examples: product recommendation, fraud detection, and customer behavior analysis.

5. Classification & Clustering

• Classification: Supervised learning techniques (Decision Trees, Naïve Bayes, K-Nearest Neighbors, Logistic Regression).
• Clustering: Unsupervised methods (K-Means, Hierarchical Clustering, DBSCAN).
• Evaluating model performance (accuracy, precision, recall, F1-score, silhouette score).
• Hands-on exercises with real datasets.

6. Anomaly Detection & Outliers

• Importance of detecting anomalies in data.
• Statistical methods for outlier detection (Z-Score, IQR).
• Machine learning approaches for anomaly detection.
• Applications: fraud detection, cybersecurity, healthcare monitoring.

7. Data Warehousing & Applications / Project Work

• Introduction to data warehousing concepts.
• ETL (Extract, Transform, Load) process.
• OLAP (Online Analytical Processing) for multidimensional analysis.
• Practical project: (data collection → cleaning → mining → visualization → reporting).
• Students present their projects demonstrating real-world applications.

After completing this module, you will be able to achieve these certifications

• By completing this course, learners will:
• Understand the fundamentals and applications of data mining.
• Collect and preprocess raw data from multiple sources.
• Apply association rule mining for discovering meaningful patterns.
• Use classification and clustering algorithms for data modeling.
• Detect anomalies and handle outliers effectively.
• Gain hands-on experience through a real-world project involving data warehousing and analysis.