Data Analytics is the most demanding technology across all the Tech. Companies, It will give a boost to your careeer.

Benifits of Data Analytics Training

  • Data analytics jobs are in high demand and high paying.
  • We provide personalized training so it will be customized for better learning experience.
  • We also provide Refferals and Interview support as Project Managers will take your mock interviews.
  • You can get upto 12 LPA – 18 LPA Package for your 1st Job (as a Fresher) even you are from private engineering college because skills matters today.
  • You will Get Authentic Certification of Data Analytics Master Course that will be authorized by most of the MNC's.

Course Description and Objectives

Data analytics helps companies gain more visibility and a deeper understanding of their processes and services. It gives them detailed insights into the customer experience and customer problems. By shifting the paradigm beyond data to connect insights with action, companies can create personalized customer experiences, build related digital products, optimize operations, and increase employee productivity.

Prerequisites

No Prerequisites Required for this Training. (Everything starts from Scratch to Advanced)

Syllabus of the Course

1. Data Handling

  1. • Introduction to Data Handling
  2. • Formatting and Coditional Formatting
  3. • Data Sorting, Filtering and Data Validation
  4. • Understanding of Name Ranges
  5. • How to work on raw data ?
  6. • Conversion of data

2. Excel and Advanced Excel

  1. • Descriptive Functions: SUM, COUNT, MIN, MAX, AVERAGE, MEAN, MEDIAN, CONCATENATE
  2. • Logical functions : IF, AND, OR, NOT
  3. • Relational Operators
  4. • Nesting of Functions
  5. • Date and Time Functions : today, now, month, year, month, year, day etc.
  6. • Text Functions : left, right, mid, find, length, replace, substitute, trim, rank etc.
  7. • Array Functions : SUMIF, SUMIFS, COUNTIF, COUNTIFS, SUMPRODUCT
  8. • Use and applications of VLOOKUP
  9. • Limitation of Lookup function
  10. • Using Index, Match, OffSet, Concept of reverse lookup
  11. • Data Analysis using Pivot tables - Use of row and column shelf, values and filters.

3. SQL/PLSQL

  1. • Schema - Meta Data - ER Diagram.
  2. • Looking at an example of database design
  3. • Data Integrity Constraints & Types of Relationships(Primary & Foriegn Key)
  4. • Basic Concepts : Queries, Data Types & NULL Values, Operators and Comments in SQL
  5. • Introduction MS SQL Server for Windows OS
  6. • Data Based Object Creation (DDL Commmands)
  7. • Creating, Modifying and deleting Databases and Tables
  8. • Drop and Truncate Statement : Uses and Differences
  9. • Alter table and Alter column statements
  10. • Data Manipulation (DML Commands)
  11. • Insert, Update and Delete Statements
  12. • Select Statement : Subsetting, Filters, Sorting, Removing, Duplicates, Grouping and Aggregation Functions
  13. • WHERE, GROUP BY, ORDER BY and HAVING clauses
  14. • SQL Functions : Number, TEXT, Date etc.
  15. • SQL Keywords : Top, Distinct, Null etc.
  16. • SQL Operators : Relational(Single-Valued and Multi Valued), Logical(AND, OR, NOT)
  17. • Accesing data from Multiple Tables using SELECT
  18. • Append and Joins
  19. • UNION and UNION ALL - Use and Constraint
  20. • Table Joins : INNER, LEFT, RGHT, FULL
  21. • Inline views and Sub Queries
  22. • Optimizing Queries

4. Statistics and Probability

  1. • Introduction to Statistics
  2. • Measures of central tendencies
  3. • Measures of variance Measures of frequency
  4. • Measures of Rank
  5. • Basics of Probability, distributions
  6. • Conditional Probability (Bayes Theorem)

5. Data Analytics with Python

  1. • Overview of Python- Starting with Python
  2. • Why Python for data science?
  3. • Introduction to installation of Python
  4. • Introduction to Python IDE's (Jupyter,/ Ipython)
  5. • Concept of Packages - Important packages
  6. • NumPy, SciPy, sci-kit-learn, Pandas, matplotlib, etc
  7. • Installing & and loading Packages & and name Spaces
  8. • Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
  9. • List and Dictionary Comprehensions
  10. • Variable & Value Labels - Date & Time Values
  11. • Basic Operations - Mathematical/string/date
  12. • Control flow & and conditional statements
  13. • Debugging and code profiling
  14. • Python Built-in Functions (Text, numeric, date, utility functions)
  15. • Concept of applying functions
  16. • Python - Objects - OOPS concepts
  17. • How to create & call class and modules?

6. Data Analytics with Power BI

  1. • Introduction to Power BI
  2. • Installing to Power BI Desktop
  3. • Various Options in Power BI Desktop
  4. • Views in Power BI Desktop
  5. • Connect and Retrieve data from different sources (CSV, Excel, etc.)
  6. • Query editor in Power BI
  7. • Power Query for cleaning the data
  8. • Power Query Functions - Text, Date, Numeric
  9. • Power Query Conditional Columns
  10. • Clean and transform data with Query Editor Define data granularity
  11. • Combining data - Merging & Appending
  12. • Reports Development (Visuals in Power BI)
  13. • Introduction to work with Power BI visuals
  14. • Reports Development in Power BI Working with Different Visuals /Charts Formatting Options in Reports
  15. • Use Different Charts with Power BI according to requirements
  16. • Use a slicer to filter visualizations
  17. • Working with Filters (Page Level, Include/Exclude, Report Level, Cross report Filter)
  18. • Download & use Custom Visuals from the Gallery
  19. • Optimizing for Performance