Data Analytics

Course Rate: INR 30000.00

Duration: 4 Months

About the Course:

Core Topics in a Data Analytics Course
1. Foundations of Data Analytics

Introduction to Data Analytics: Definitions, types (descriptive, diagnostic, predictive, prescriptive), analytics lifecycle

Data-driven decision-making, ethics, governance, and compliance regulations (e.g., GDPR)

Sources: Common overview modules found in programs like Scaler and Google Certificate
Scaler
Grow with Google

2. Business Statistics & Data Handling

Descriptive statistics: mean, median, standard deviation, data distributions

Probability fundamentals, hypothesis testing, confidence intervals

Inferential statistics: correlation, regression, ANOVA, chi-square tests

Data cleaning: handling missing values, imputation, outlier detection

Sources: Detailed syllabus outlines (e.g., PwSkills)
PW Skills

3. Spreadsheet Analytics (e.g., Excel)

Basic to advanced functions: text formulas, conditional logic, data validation

Formatting, pivot tables, slicers, charts, and dashboard essentials

Introduction to Power Query & Power Pivot for ETL and data modeling

Sources: Hands-on modules outlined by PwSkills and Masai School
PW Skills
Masai School

4. Database Management & SQL

Database basics: relational data, schema design

SQL querying: SELECT, WHERE, JOINs, subqueries, aggregations, window functions

Sources: UT Austin and other courses include SQL fundamentals prominently
onlineexeced.mccombs.utexas.edu
PW Skills

5. Programming for Data Analytics

Python: NumPy, Pandas, data cleaning, exploratory data analysis (EDA), Seaborn/Matplotlib visualizations

R: Tidyverse, data wrangling, visualization with ggplot2, statistical modeling

Sources: Modules included in UT Austin, Google, Eastern University programs
onlineexeced.mccombs.utexas.edu
Grow with Google
Eastern University

6. Data Visualization Tools

Tableau: Dashboard creation, calculated fields, filters, interactivity

Power BI: Report building, dashboards, publishing and sharing analytics

Visual storytelling best practices: choosing chart types, use of color, narrative flow

Sources: Covered across multiple syllabi
Masai School
PW Skills
onlineexeced.mccombs.utexas.edu

7. Machine Learning & Predictive Analytics (Intro)

Statistical modeling: linear and logistic regression, model diagnostics

Basics of classification, clustering, and feature engineering

Optional: introductory machine learning—decision trees, SVMs, neural networks

Sources: Eastern University, Binghamton University, Masai School syllabus sections
Eastern University
Binghamton University
Masai School

8. Hands-On Projects & Practicums

Excel dashboards: visually communicate business insights

SQL projects: querying real datasets (e.g., sales, marketing)

Python/R notebooks: data cleaning, EDA, basic modeling

Visualization final project with Tableau/Power BI

Capstone/real-world data challenges: integrate ETL, analysis, visualization

Sources: Practicum details from Binghamton University
Binghamton University

9. Complementary Skills & Career Preparedness

Data storytelling: structuring insights into reports and presentations

Soft skills: critical thinking, communication, collaboration

AI enhancements: using AI tools to streamline analytics workflows

Career readiness: resume building, portfolio review, mock interviews

Sources: Google Certificate program includes storytelling and AI elements