DATA ANALYTICS

Module 1: Introduction to Data Analytics

• Understanding Data Analytics: Concepts, importance, and applications
• Types of Data: Structured vs. unstructured data
• Data Collection Methods and Sources
• Career Scope and Industry Applications of Data Analytics
• Business Analytics Integration and Data-Driven Decision Making

Module 2: Data Analysis Using Statistics and Probability

• Descriptive Statistics: Mean, Median, Mode, Variance, Standard Deviation
• Probability Concepts: Basics, Bayesian Inference, Probability Distributions (Normal, Binomial)
• Examples of statistical inference include hypothesis testing, point estimation, and confidence intervals
• Regression Analysis: Residual Analysis, Logistic and Linear Regression
• Data Clustering and Association Rules
• Experimental Design and A/B Testing

Module 3: Excel for Data Analysis

• Basic Excel Functions: Formulas, Pivot Tables, Filtering, Sorting
• Advanced Excel Proficiency: Power Pivot, Power Query, and VBA Macros
• Data Cleaning and Preprocessing in Excel
• Creating Dynamic Dashboards and Reports

Module 4: SQL and Database Management

• Introduction to Databases and SQL
• Writing Queries: SELECT, INSERT, UPDATE, DELETE
• Joins, Subqueries, and Complex Query Building
• Database Normalization and Indexing
• Working with Large Datasets and Optimization Techniques

Module 5: Python and R Programming for Data Analysis

• Basics of Python: Loops, Variables, Functions, and Data Types
• NumPy, Pandas, Matplotlib, and Seaborn for data manipulation and visualization
• Python Data Cleaning and Wrangling
• Overview of R Programming: Import/Export of Data, Functions, and Data Types
• R Data Visualization: Box Plots, Scatter Plots, and Histograms
• Python and R for Exploratory Data Analysis (EDA)

Module 6: Tools for Business Intelligence and Data Visualization

• Tools: Tableau, Power BI
• Creating Interactive Dashboards and Reports
• Data Blending, Filters, and Calculations in Visualization Tools
• Storytelling with Data and Presentation Techniques

Module 7: Machine Learning and Predictive Analytics

• Introduction to Machine Learning Concepts
• Supervised Learning: Decision Trees, Random Forest, K-Nearest Neighbors
• Unsupervised Learning: K-Means Clustering
• Model Evaluation and Validation Techniques
• Time Series Forecasting and Predictive Modeling

Module 8: Big Data and Cloud Computing Basics

• Introduction to Big Data Concepts and Technologies
• Working with Hadoop and Spark Ecosystem
• Cloud Platforms Overview: AWS, Azure, Google Cloud
• Querying Big Data on Cloud Platforms using tools like Google BigQuery and Snowflake

Module 9: Advanced Topics and Tools

• AI and Automation in Data Analytics
• Data Ethics and Security
• Blockchain Analytics
• SAS Programming: Data Steps, Procedures, Macros
• Integration of Tools: Combining Tableau, R, and Hadoop for Advanced Analytics

Module 10: Project and Internship

• Real-world Data Analysis Projects
• Industry Internship Experience
• Application of Learned Skills to Business Problems
• Presentation and Reporting of Findings

Experience in the Industry Gain knowledge from experts with practical Data Analyst project experience in a variety of sectors.

Backgrounds at the Top Prominent corporations such as hcl, tcs , accenture  and cognizant  employ trainers.

Clear & Effective Teaching Excellent communication and real-world examples simplify complex subjects.

Hands-On Learning Focus Students can apply their abilities in real-world situations with the use of case studies and real-time projects.

Up-to-Date Knowledge Trainers stay current with the latest tools, techniques and best practices.