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Master in Data Analytics Course

Master in Data Analytics

78%

Alumni Career Transitions

5200+

Hiring Partners

60%

Avg Salary Hike

22

Years of R & D in Syllabus

Key Features:

  • Experiential Learning
  •  Offline/Online Classes
  • 20+ practices sessions on all modules
  • Case studies and assignments
  • Hands on Projects
  • Mentoring Sessions
  • Job Assistance
Learning Pathway:
  • Learn python program from scratch.
  • Statistical and mathematical essential for Data Science
  • Data Science with python
  • Data Science with R
  • Data visualization with Tableau and Power BI
Program Outcomes:
  • Deep understanding of data structure and data manipulation.
  • Gain expertise in mathematical computing using the NumPy.
  • Gain expertise in Exploratory data analysis using pandas, matplotlib and seaborn.
  • Gain expertise in time series modeling

Programming is an increasingly important skill; this program will establish your proficiency in handling basic programming concepts. By the end of this program, you will understand object-oriented programming; basic programming concepts such as data types, variables, strings, loops, and functions; and software engineering using Python

Objectives:

  • Gain fundamental knowledge of programming basics.
  • Achieve an understanding of object-oriented programming principles including data types,
    variables, strings, loops, strings, lists, functions, and classes etc.
  • Comprehend software engineering concepts, using Python

Program Curriculum:

  • Course Introduction
  • Programming

Statistics is the science of assigning a probability through the collection, classification, and analysis of data. A foundational part of Data Science, this session will enable you to define statistics and essential terms related to it, explain measures of central tendency and dispersion, and comprehend skewness, correlation, regression, distribution. Understanding the data is the key to perform Exploratory Data analysis and justify your conclusion to the business or scientific problem.

Objectives:

  • Understand the fundamentals of statistics
  • Work with different types of data
  • How to plot different types of data
  • Calculate the measures of central tendency, asymmetry, and variability
  • Calculate correlation and covariance
  • Distinguish and work with different types of distribution
  • Estimate confidence intervals
  • Perform hypothesis testing
  • Make data-driven decisions
  • Understand the mechanics of regression analysis
  • Carry out regression analysis
  • Use and understand dummy variables
  • Understand the concepts needed for Data Science even with Python

Program Curriculum:

  • 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 Lesson
  • Practical Example: Inferential Statistics
  • Hypothesis Testing: Introduction
  • Practical Example: Hypothesis Testing
  • The Fundamentals of Regression Analysis
  • Assumptions for Linear Regression Analysis
  • Dealing with Categorical Data
  • Practical Example: Regression Analysis

Perform fundamental hands-on data analysis using the Jupyter Notebook and PyCharm based lab environment and create your own Data Science projects learn the essential concepts of Python programming and gain in-depth knowledge in data analytics, Machine Learning, data visualization, web scraping, and natural language processing. Python is a required skill for many Data Science positions.

Objectives:

  • Write your first Python program by implementing concepts of variables, strings, functions, loops, conditions
  • Understand the concepts of lists, sets, dictionaries, conditions and branching, objects and classes .
  • Work with data in Python such as reading and writing files, loading, working, and saving data with Pandas
  • Gain an in-depth understanding of Data Science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing.
  • Install the required Python environment and other auxiliary tools and libraries.
  • Understand the essential concepts of Python programming such as data types, tuples, lists, dictionaries, basic operators and functions.
  • Perform high-level mathematical computing using the NumPy package and its vast library of mathematical functions.
  • Perform data analysis and manipulation using data structures and tools provided in the Pandas package.
  • Gain expertise in Machine Learning using the Scikit-Learn package
  • Gain an in-depth understanding of supervised learning and unsupervised learning models such as linear regression, logistic regression, clustering, dimensionality reduction, K-NN and pipeline
  • Use the matplotlib library of Python for data visualization
  • Extract useful data from websites by performing web scraping using Python.

Program Curriculum:

  • Python Basics
  • Python Data Structures
  • Python Programming Fundamentals
  • Working with Data in Python
  • Data Science Overview
  • Data Analytics Overview
  • Statistical Analysis and Business Applications
  • Python Environment Setup and Essentials
  • Mathematical Computing with Python (NumPy)
  • Data Manipulation with Pandas
  • Machine Learning with Scikit–Learn
  • Natural Language Processing with Scikit Learn
  • Data Visualization in Python using Matplotlib
  • Web Scraping with Beautiful Soup
  • Working with NumPy Arrays

A database is an organized collection of structured information, or data, typically stored electronically in a computer system. A database is usually controlled by a database management system (DBMS). Company data are store in databases and later on retrieved using python to develop analytics and bring insights to business problems.

Objectives:

  • Understand the basic fundamentals of SQL database
  • Methods to structure and configure your database
  • Structure the author efficient SQL statements and clauses Manage your SQL database

Program curriculum:

  • Introduction to SQL
  • Database Normalization and Entity-Relationship (ER) Mode
  • Installation configurations to setup MySQL
  • Understanding Database and Tables
  • Learn Operators, Constraints, and Data Types
  • Understanding functions, Subqueries, Operators, and Derived Tables in SQL

It will make you an expert in Machine Learning, a subclass of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific  tasks without explicit programming. You will master Machine Learning concepts and techniques, including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms and prepare you for your role with advanced Machine Learning knowledge.

Objectives:

  • Master the concepts of supervised and unsupervised learning, recommendation engine, and time series modeling
  • Gain practical mastery over principles, algorithms, and applications of Machine Learning through hands-on projects
  • Acquire thorough knowledge of the statistical and heuristic aspects of Machine Learning
  • Implement models such as support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-means clustering and more in Python
  • Validate Machine Learning models and decode various accuracy metrics. Improve the final models using another set of optimization algorithms
  • Comprehend the theoretical concepts and how they relate to the practical aspects of Machine Learning

Program Curriculum:

  • Introduction to Artificial Intelligence and Machine Learning
  • Data Wrangling and Manipulation
  • Supervised Learning
  • Feature Engineering
  • Supervised Learning Classification
  • Unsupervised Learning
  • Time Series Modeling
  • Ensemble Learning
  • Recommender Systems
  • Text Mining

The Data Science with R enables you to take your data science skills to solve multiple problems with statistical and related libraries.  The course makes you skilled with data wrangling, data exploration, data visualization, predictive analytics, and descriptive analytics techniques. You will learn about R from basics with installation to import and export data in R, data structures in R, various statistical concepts, cluster analysis, and forecasting.

Objectives:

  • Understanding of business analytics with R
  • Installation of R with RStudio, workspace setup, and learn about the various R packages
  • Understanding of data structure used in R and learn to import/export data in R Define
  • Understand and use the various graphics in R for data visualization Gain
  • Gain knowledge in various statistical concepts
  • Understand and learn to develop linear and nonlinear models for multiple scenarios.

Program Curriculum:

  • Introduction to Business Analytics use cases and concepts
  • Introduction to R Programming with datatypes, operators, statements, functions etc
  • Understanding data structures and various visualization techniques with Ggplot2
  • Learn basics of statistics for data science pipeline

Business Analytics with Excel training will boost your analytics career with powerful Excel skills. This business analytics training course will equip you with the concepts and hard skills required for a strong analytics career. You’ll learn the basic concepts of data analysis and statistics, helping promote data-driven decision making. Your new knowledge of this commonly used tool combined with official business analytics is guaranteed to ensure career success.

Objectives:

  • Understand the meaning of business analytics and its importance in the industry
  • Solve stochastic and deterministic analytical problems using tools
  • Grasp the fundamentals of Excel analytics functions and conditional formatting
  • Learn how to analyze with complex datasets using pivot tables and slicers
  • Represent your findings using charts and dashboards.

Program Curriculum:

  1. Introduction to Business Analytics
  2. Formatting Conditional Formatting and Important Functions
  3. Analyzing Data with Pivot Tables
  4. Dashboarding
  5. Business Analytics with Excel
  6. Data Analysis Using Statistics

 

 

Data Science with Tableau helps to see and understand data solving various business problems. Our visual analytics platform is transforming the way people use data to solve problems. Course enables you to create visualizations, organize data, and design plots and develop dashboards to bring more insights to the problem. Learn various concepts of Data Visualization, combo charts, working with filters, parameters, and sets, and building interactive dashboards.

Objectives:

  • Understand various visualization techniques
  • Understand metadata and its usage Work with Filter, Parameters, and Sets Master special field types and Tableau-generated fields
  • Understanding the data visualization pipeline with tableau
  • Build charts, interactive dashboards and concepts of data blending, create data extracts and organize and format.

Program Curriculum:

  • Learn to perform data visualization from scratch from installation to data loading and interface bring up
  • Understanding Discrete and Continuous values and applications
  • Aggregations in tableau
  • – Creating Charts in Tableau Bar Char, Line Chart, Scatter Plots, Dual-Axis Charts Combined-Axis Charts, Funnel Chart, Cross Tabs Highlight, Tables Maps
  • Understanding data and various techniques to rename, Hide, Sort Columns and field properties
  • Learn basics of Filters in Tableau
  • Performing data Analytics in sheets
  • Creating interactive Dashboards in Tableau

This Power BI deals with how to handle multiple data sources, extract them perform various data filtering, manipulations, understanding the patterns in data and create customized dashboards with powerful developer tools It is suitable for business intelligence (BI) and reporting professionals, data analysts, and professionals working with data in any sector.

 Objectives:

  • Understand Power BI concepts like Microsoft Power BI desktop layouts, BI reports, dashboards, and Power BI DAX commands and functions
  • Create customized visuals and deliver a reliable analysis of vast amount of data using Power BI

Program Curriculum:

  • Understand various data streams or sources, and implement the pipeline into Power BI
  • Exploring various functionalities and understand data patterns
  • Create custom dashboards to various projects
  • Creating an Analysis or Management Reports.

Technologies:

Python:

Introduction to Python and Computer Programming, Data Types, Variables, Basic Input-Output Operations, Basic Operators, Boolean Values, Conditional Execution, Loops, Lists and List Processing, Logical and Bitwise Operations, Functions, Tuples, Dictionaries, Sets, and Data Processing, Modules, Packages, String and List Methods, and Exceptions, File Handlings. Regular expressions, the Object-Oriented Approach: Classes, Methods, Objects, and the Standard Objective Features; Exception Handling, and Working with Files.

R:

R Introduction, Data Inputting in R, Strings,Vectors, Lists, Matrices, Arrays Functions and Programming in R, Data manipulation in R, Factors, DataFrame, Packages, Data Shaping, R-Data Interface, Web Dataand Database, Charts-Pie, Bar Charts, Boxplots, Histograms, LineGraphs, Mean, Median and Mode, Regression- Linear, Multiple, Logistic, Poisson, Distribution-Normal, Binomial, Analysis-Covariance, Time Series, Survival, Nonlinear Least Square, DecisionTree, Random Forest 

MySQL

MySQL – Introduction, Installation, Create Database, Drop Database, Selecting Database, Data Types, Create Tables, Drop Tables, Insert Query, Select Query, WHERE Clause, Update Query, DELETE Query, LIKE Clause, Sorting Results, Using Joins, Handling NULL Values, ALTER Command, Aggregate functions, MySQL Clauses, MySQL Conditions.

Matplotlib:

Scatter plot, Bar charts, histogram, Stack charts, Legend title Style, Figures and subplots, Plotting function in pandas, Labelling and arranging figures, Save plots.

Seaborn:

Style functions, Color palettes, Distribution plots, Categorical plots, Regression plots, Axis grid objects.

NumPy

            Creating NumPy arrays, Indexing and slicing in NumPy, Downloading and parsing data Creating multidimensional arrays, NumPy Data types, Array attributes, Indexing and Slicing, Creating array views copies, Manipulating array shapes I/O.

Pandas:

Using multilevel series, Series and Data Frames, Grouping, aggregating, Merge Data Frames, Generate summary tables, Group data into logical pieces, manipulate dates, Creating metrics for analysis, Data wrangling, Merging and joining, Data Mugging using Pandas, Building a Predictive Mode.

Scikit-learn:

            Scikit Learn Overview, Plotting a graph, Identifying features and labels, Saving and opening a model, Classification, Train / test split,   What is KNN? What is SVM?, Linear regression, Logistic vs  linear regression, KMeans, Neural networks, Overfitting and underfitting, Backpropagation, Cost function and gradient descent, CNNs

Excel

Excel Introduction, Worksheets in Excel, Rows, Columns, Overview, Syntax, Ranges, Fill, Move Cells, Add Cells, Delete Cells, Formulas, Addition Operator, Parentheses, Functions, Excel Formatting, Excel Calculations, Excel Data Analysis, Sort, Filter, Excel Tables, Conditional Formatting, Excel Charts, Excel PivotTable,  Excel Functions.

Tableau

Tableau Architecture, File Types, Data Types, Tableau Operator, String Functions, Date Functions Logical Functions, Aggregate Functions, Joins in Tableau, Types of Tableau Data Source, Data Extracts, Filters, Sorting, Formatting, Adding Worksheets and Renaming Worksheet In Tableau, Tableau Save, Reorder and Delete Worksheet, Charts, dashboard.

Power BI

Power BI Architecture, Components, Power BI Desktop, Connect to Data in Power BI Desktop, Data Sources for Power BI, DAX in Power BI, Q & A in Power BI, Filters in Power BI, Power BI Query Overview, Creating and Using Measures in Power, Calculated Columns, Data Visualizations, Charts, Area, Funnel, Combo, Donut, Waterfall, Line, Maps, Bar, KPI, Power BI Dashboard.

** Syllabus Updated on April 2023 -2024

Best Master in Data Analytics Training and Certification 2023 Online Offline classes Virtual Lab Facility Updated syllabus Computer Hardware

** The above is the lite syllabus and doesn’t cover the full syllabus. To get full syllabus  Book a Free Demo Now

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Flexible Schedule

Set and maintain flexible deadlines.

Certifications & Accreditations

NSDC 2 - Master in Data Analytics Course
iisc 1 - Master in Data Analytics Course
Zohobooks + Quickbooks
Diploma in Fire and Industrial Safety Management
ESSI - Master in Data Analytics Course
CGSC - Master in Data Analytics Course

Benefits of learning from us

Program Fees

Live Instructor Led Training Fee

 162,500.00
  • The above fees are applicable to candidates in India only.

Mode of Training

OnDemand

Live Instructor Led

Virtual Lab

Classroom

Comprehensive Curriculum

4 months +

Learning Content + Practicals

Regular Batch

Date

24-Jun-2024

Time

10:30 AM IST

Fast Track Batch

Date

26-Jun-2024

Time

10:30 AM IST

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