Nex-G Innovations

Data analytics is important because it helps businesses optimize their performances. Implementing it into the business model means companies can help reduce costs by identifying more efficient ways of doing business and by storing large amounts of data.
A company can also use data analytics to make better business decisions and help analyze customer trends and satisfaction, which can lead to new—and better—products and services.

Code: NES_SK_22969

Duration: 200 Hrs / 20 Weeks / Customized

Mode: Online / Offline / Onsite

+91-8826900551

• Introduction
• Sample or population data?
• The fundamentals of descriptive statistics
• Measures of central tendency, asymmetry, and variability
• Practical example: descriptive statistics
• Distributions
• Estimators and estimates
• Confidence intervals: advanced topics
• Practical example: inferential statistics
• Hypothesis testing: Introduction
• Hypothesis testing: Let’s start testing!
• Practical example: hypothesis testing
• The fundamentals of regression analysis
• Subtleties of regression analysis
• Assumptions for linear regression analysis
• Dealing with categorical data
• Practical example: regression analysis

Module 2 - Programming Fundamental

• Course Introduction
• Basics of Java Python C++

Module 3 - SQL Basics

• Rational Databases
• SQL Querying
• Your First Queries
• Filtering Your Results
• Consolidating Your Data
• Grouping Your Data
• Joining Tables
• Subqueries
• Manipulating Your Data
• Transaction Control
• Creating Database Objects and Adding Business Logic

Module 4 - Python for Data Science

• Python Basics
• Python Data Structures
• Python Programming Fundamentals
• Working with Data in Python
• Working with NumPy Arrays

Module 5 - Data Analysis with Python

• Python Basics
• Importing Datasets
• Cleaning and Preparing the Data
• Summarizing the Data Frame
• Model Development
• Model Evaluation
• Types of Data Formats

Module 6 - Data Visualization with Python

• Introduction to Visualization Tools
• Basic Visualization Tools
• Specialized Visualization Tools
• Advanced Visualization Tools
• Creating Maps and Visualizing Geospatial Data

Module 7 - Introduction to R Programming

• R Basics
• Data Structures in R
• R Programming Fundamentals
• Working with Data in R
• Stings and Dates in R

Module 8 - Data Visualisation with R

• Basic Visualization Tools
• Basic Visualization Tools Continued
• Specialized Visualization Tools
• How to Create Maps
• How to Build Interactive Web Pages

Module 9 - Data Science with R

• Introduction to Business Analytics
• Introduction to R Programming
• Data Structures
• Data Visualization
• Statistics for Data Science-I
• Statistics for Data Science-II
• Regression Analysis
• Classification
• Clustering
• Association

Module 10 - Business Analytics with Excel

• Introduction to Business Analytics
• Formatting Conditional Formatting and Important Functions
• Analysing Data with Pivot Tables
• Dashboarding
• Business Analytics with Excel
• Data Analysis Using Statistics
• Power BI

Module 11 – Tableau

• Getting Started With Tableau
• Working With Tableau
• Deep Diving with Data and Connections
• Creating Charts
• Adding Calculations to your Workbook
• Mapping data in Tableau
• Dashboards and Stories
• Visualizations for an Audience

Module 12 - Power BI

• Create dashboards from published reports
• Quickly generate visuals and dashboards with Quick Insights
• Use natural language in the Q&A feature to quickly generate visuals for actionable insight
• Create and manage data alerts
• Get report layout and data visualization best practices
• Understand which charts/graphs to use depending on the question being answered or the story being told
• Use shapes to design, emphasize, and tell a story
• See how to incorporate custom visuals into your reports and dashboards
• Share reports and dashboards, as well as their pro’s and con’s
• Complete a Power BI data analysis/visual project from start to finish