DATA ANALYTICS WITH PYTHON

Introduction

The Data Analytics course is designed to equip learners with the skills to analyse, interpret, and make data-driven decisions. Covering key concepts, tools, and techniques, this course combines theoretical knowledge with practical applications, allowing participants to understand and leverage data analytics effectively across various domains. From foundational concepts to advanced techniques, learners will gain the confidence to tackle data analytics tasks with proficiency.

Course Objectives

Understand the fundamentals of data analytics and its real-world applications.
Master data collection, cleaning, and processing techniques.
Gain hands-on experience with popular data analytics tools and libraries.
Develop skills in data visualization and storytelling with data.
Learn statistical and machine learning methods to uncover insights and patterns.
Build and deploy data models for predictive analytics.

Syllabus Outline

Module 1: Introduction to Data Analytics

Overview of Data Analytics
Types of Data Analytics (Descriptive, Diagnostic, Predictive, Prescriptive)
Applications of Data Analytics in Business and Technology
Introduction to Data Analytics Tools (Excel, Python, R, SQL)

Module 2: Data Collection and Pre-processing

Data Collection Techniques
Introduction to Data Wrangling and Cleaning
Handling Missing Data, Duplicates, and Outliers
Data Transformation and Standardization
Exploratory Data Analysis (EDA)

Module 3: Statistics for Data Analysis

Basic Statistical Concepts
Probability Theory and Distributions
Hypothesis Testing
Confidence Intervals and P-Values
Correlation and Causation

Module 4: Data Visualization and Communication

Creating Visuals in Excel, Python (Matplotlib, Seaborn), Tableau, and PowerBI
Dashboard Design and Reporting

Module 5: Data Analytics with Python

Introduction to Python for Data Analytics
Working with Data Libraries (Pandas, Numpy)
Data Manipulation and Aggregation

Module 6: SQL for Data Analytics

Introduction to SQL
Basic SQL Queries (SELECT, WHERE, JOIN)
Data Aggregation with SQL
Advanced SQL Queries (Window Functions, Subqueries)
Connecting SQL with Python

Module 7: Machine Learning for Data Analytics

Introduction to Machine Learning Concepts
Supervised vs. Unsupervised Learning
Common Algorithms (Linear Regression, Decision Trees, Clustering)
Model Evaluation and Validation Techniques
Building and Deploying Predictive Models

Learning Outcomes

By the end of the course, learners will be able to:

Conduct end-to-end data analysis and interpret results.
Visualize and communicate findings effectively to stakeholders.
Implement statistical and machine learning models for real-world data problems.
Use Python, SQL, and Visualization tools proficiently for data analysis tasks.

Course Duration

12 Weeks (with options for full-time or part-time study)