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ai 5.avif

DATA ANALYSTS COURSE

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  • Foundations & Tools: Learn Python, data handling, statistics, and essential libraries like Pandas, NumPy, Matplotlib, and Git for version control.

  • Core Data Science & ML: Master EDA, machine learning, deep learning, NLP, and model evaluation techniques with real-world applications.

  • Deployment & Capstone: Build, deploy, and present a complete end-to-end project using cloud platforms, APIs, Docker, and GitHub.

Course 
Syllabus

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Syllabus

1

Foundations

  1. Introduction to Data Analytics

  2. Role of a Data Analyst

  3. Types of data: structured vs unstructured

2

Excel / Spreadsheets

  1. Formulas, Functions (VLOOKUP, IF, INDEX/MATCH)

  2. Pivot Tables and Charts

  3. Data cleaning and formatting

3

Python for Data Analysis

  1. Python basics (variables, loops, functions)

  2. Libraries: pandas, numpy, matplotlib, seaborn

  3. Data wrangling and visualization

4

Statistics & Probability

  1. Descriptive stats: mean, median, mode, std dev

  2. Probability concepts and distributions

  3. Hypothesis testing and confidence intervals

5

SQL

  1. SELECT, WHERE, GROUP BY,

  2. JOINsAggregations and subqueries

  3. Working with large datasets

6

Data Visualization

  1. Tools: Tableau / Power BI / Python (Matplotlib/Seaborn)

  2. Charts: bar, line, scatter, heatmap, dashboards

  3. Design principles and storytelling with data

7

Data Cleaning & Preparation

  1. Handling missing data and duplicates

  2. Data transformation and normalization

  3. Feature engineering basics

8

Tools & Workflow

  1. Git & GitHub basics

  2. Jupyter Notebooks / Google Colab

  3. Data pipelines (intro to ETL)

9

Projects & Case Studies

  1. Real-world datasets (e.g., sales, customer behavior)

  2. End-to-end analysis project

  3. Reporting and presenting insights

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