Data Analytics
About This Course
The Data Analytics Program is designed to equip students with the skills to collect, process, analyze, and visualize data to drive business decisions. The program covers key areas such as statistics, data wrangling, machine learning, business intelligence, and big data technologies.
Students will gain hands-on experience with tools like Python, R, SQL, Tableau, Power BI, and ML, preparing them for real-world data challenges.
Why Choose This Program?
- High-Demand Skills: Data analysts are needed in every industry, from healthcare to finance.
- Hands-on Projects: Work with real datasets and case studies.
- Industry-Aligned Certifications: Prepares students for Google, Microsoft, and AWS certifications.
- Expert-Led Training: Learn from experienced data scientists and analysts.
- Career Support: Resume building, interview prep, and job placement assistance.
Who Should Enrol
- Students preparing for data-related certifications.
- Beginners looking to start a career in data analytics.
- IT/Finance/Marketing professionals transitioning into analytics.
- Business professionals who want to leverage data for decision-making.
Curriculum
11 Modules
Overview of Data Analytics & its Importance
Types of Data Analytics (Descriptive, Diagnostic, Predictive, Prescriptive)
Data Analytics Lifecycle
Tools & Technologies in Data Analytics (Python, R, SQL, Excel, Power BI, Tableau)
Industry Applications (Finance, Healthcare, Marketing, etc.)
Data Sources (Structured & Unstructured Data)
Web Scraping & APIs for Data Collection
Data Cleaning (Handling Missing Values, Outliers, Duplicates)
Data Transformation (Normalization, Standardization, Encoding)
Feature Engineering
Descriptive Statistics (Mean, Median, Variance, Skewness, Kurtosis)
Data Visualization (Matplotlib, Seaborn, Plotly)
Univariate & Multivariate Analysis
Correlation & Heatmaps
Identifying Patterns & Anomalies
Probability Distributions (Normal, Binomial, Poisson)
Hypothesis Testing (t-tests, Chi-square, ANOVA)
Confidence Intervals & p-values
A/B Testing & Experimental Design
Bayesian Statistics Basics
Database Fundamentals (Relational vs. NoSQL)
SQL Queries (SELECT, JOIN, GROUP BY, Subqueries)
Aggregation Functions (SUM, AVG, COUNT)
Window Functions (RANK, PARTITION BY)
Optimizing Queries for Performance
Python Basics for Data Analysis (Pandas, NumPy)
Data Manipulation (Filtering, Sorting, Merging)
Advanced Python for Analytics (Lambda Functions, List Comprehensions)
Introduction to R (dplyr, ggplot2)
Supervised vs. Unsupervised Learning
Regression (Linear, Logistic)
Classification (Decision Trees, Random Forest, SVM)
Clustering (K-Means, Hierarchical)
Model Evaluation (Accuracy, Precision, Recall, ROC Curve)
Principles of Effective Data Visualization
Tools: Power BI, Tableau, Google Data Studio
Creating Interactive Dashboards
Storytelling with Data
Introduction to Big Data (Hadoop, Spark)
Cloud Platforms (AWS, Google Cloud, Azure for Analytics)
Handling Large Datasets Efficiently
KPI & Metrics Tracking
Data-Driven Decision Making
Case Studies (Retail, Finance, Healthcare)
Ethics & Privacy in Data Analytics
End-to-End Data Analytics Project (Real-world Dataset)
Problem Definition → Data Collection → Analysis → Insights → Presentation
Mock Interviews & Resume Building
Preview This Course
Rs. 15,999/-
Rs. 30,999/-
Job Profile
- Data Analyst
- Business Analyst
- BI Developer
- Marketing Analyst
- Data Consultant
Program Structure
- Duration: 4 Months
- Format: Live Lectures
- Schedule: Weekend OR Weekdays
- Capstone Projects
- LMS Access of Recorded Sessions
Placement Support
- Mock Interview Preparation
- Cover Letter & Resume Building
- Professional Social Profile Building
- HR Connections
- Interview Line-up
Learning Outcome
- Clean and transform raw data into actionable insights
- Build interactive dashboards (Tableau/Power BI)
- Write advanced SQL queries for database management
- Perform statistical analysis (A/B testing, regression)
- Present data-driven recommendations to stakeholders