Most advanced course in Data Analytics in Delhi

ADMEC Multimedia > Data Science and Analytics Courses > Data Analytics Master Course

Eligibility

Graduation Not Required

Duration

06 Months

Training Type

Classroom, Online

Most Advanced Data Analytics Course Using Statistics, Probability, SQL, Excel, R, Power BI, Tableau, Python (NumPy, Pandas, Matplotlib, SciPy, and Flask).

This 6 months (3 months in fast track) diploma course is designed for beginners to take their analytical thinking to the world’s biggest industries in our classroom and online training. We will start this course from the very basics and slowly-slowly you will be uncovering all the leading data analytical and data visualization languages and software.

As you know that Data Analytics & Visualization is one of the hottest sub-set of Data Science and data analysis using Python and R is in high demand so this course fulfills all the demands of data analysis of the industry from you.

Why we’re on Top in Data Analytics Training Delhi?

Across-the-board Curriculum
Selected Mentorship
Persistent Career Care

Tools and Technologies

Tools Covered	Statistics Excel SQL Python NumPy Pandas Matplotlib SciPy Flask R Language Power BI Tableau
Key Takeaways	Mastering Statistics in Data Analysis Elementary Data Analysis on Excel Understanding SQL As A Query Language Data Analysis Using Python and R Languages Data Analysis and Visualization Using Power BI and Tableau

We are unsure you are fully convinced with above content so might be having various queries. Down here we have specified rare and very vital topics to go over to identify more about the admissibility measures, course structure, projects accomplished, career choices etc.

Please check out our other Data Analytics courses too:

Data Analytics Premium
Data Analytics Standard

Entry requirements

Our Data Analytics Master course is for anyone who wants to learn all the leading languages and software used in data analytics and visualization to take out the powerful data points to help industries grow. You must have analytical and logical mindset with 10+2 (intermediate) to be successful in this area.

No programming experience required.

Who can join this course

Do you love huge or big data? Data handling was your area of interest in schools? Then this course is best for you to work with huge data to get some hidden success mantra which provide solutions to businesses to grow in their domains. You are welcome if you have analytical thinking with interest in programming and 10+2 (intermediate) to make businesses succeed.

Career opportunities

Students who successfully completed this course are working as Senior Data Analyst, Python Data Analyst, R Data Analyst, R Developer, Python Expert, Business Analyst, SQL Expert, Excel Expert, Power BI Expert, Tableau Professional etc.

Final projects and tests

You will be working on ‘Data Analytics Project from Scratch’ project with our data analytics expert so that you can prepare yourself to work in the most competitive industry of data analytics after completion of this analytics course.

Course structure and modules

04 Weeks: Module 1

Foundations – Basics of Data Analytics

Part 1 – Introduction

Introduction to Data Analytics
Data Analytics in Real Life
Data Analysis vs Data Analytics
Common Tools and Technologies
Data and Its Importance
Common Steps for Data Analysis

Part II – Math Refresher

Useful Concepts of Math
Use of Algebra, Geometry, and Calculus in Data Analysis!
Data Table, Charts, Graphs etc.

Part III – Statistics

Statistics in Data Analysis
Types of Statistics
Types of Data
Categorical Data
Numerical Data
Association between Categorical & Numerical Variables
Correlation and Covariance
Hypothesis Testing
Advanced Statistical Techniques
- Multivariate Regression
- Classification Problems
- Predictive Analytics
- Text Analytics

Part IV – Probability

Rules of Probability
Conditional Probability
Random Variables
Distributions
Combinatorics
Bayes Theorem
Bayesian Inference

Part V – Data Analysis and Visualization in Excel

Functions in Excel
Data Analysis
Pivot Tables and Lookup
Interactive charts
What-if-analysis, GOAL SEEK
Linking Multiple Sheets
Sub Total Reports, Auto Filter
Password Protecting Worksheets
Dashboard
Record a Macro/VBA

Part VI – Data Analytics Essentials of SQL

Introduction to DBMS
ER diagram
Schema design
Key constraints & basics of normalization
Joins
Subqueries involving joins & aggregations
Sorting
Independent subqueries
Correlated subqueries
Analytic functions
Set operations
Grouping and filtering

10 Weeks: Module 2

Data Analytics Practical

Part 1 – Data Analysis with Python

Installing and Exploring Python
Anaconda
Jupyter Notebook
NumPy
Python Pandas
Matplotlib
SciPy
Flask Framework

Part 2 – Use of R Language in Data Analysis

Exploring R Programming Fundamentals
Data Manipulation in R
Data Modelling in R
Variance, Linear Regression, and Logistic Regression in R
RStudio
Data Visualization with R
Project in R

06 Weeks: Module 3

Data Visualization and Insights

Part 1 – Exploring Power BI

Introduction to Data Visualization
Introduction to Power BI
Overview of the Rhyme Interface
Importing the Data
Fixing the Column Names
Transforming the Data
Creating Reports
Defaults by Education Level
Defaults by States
Defaulter Ratio and Slicing the Data
Project in Power BI

Part 2 – Exploring Tableau

Introduction to Tableau
Basic charts and dashboard
Descriptive Statistics, Dimensions and Measures
Visual analytics: Storytelling through data
Dashboard design & principles
Advanced design components – Improving the dashboards
Special types of chart
Integrate Tableau with Google Sheets and other data sources
Project in Tableau

04 Weeks: Module 4

Domain Exposure

Analytics for Marketing & Retail
Analytics for Web & Social Media
Analytics for Finance & Risk
Analytics for Supply Chain & Logistics