About The Course

Big Data Analytics is combination of Big Data Engineering, Business Intelligence and Data Analytics with in-depth skills in Mathematics, Statistics, Programming and other similar streams to structure large data sets. Our Big Data Analytics course teaches you how to develop a cluster with framework and store large volume of data. You learn how to create reports for historical data and identify patterns. The program also trains you to use data pipelines, pass the data into ML Libraries and predict the future.

Eligibility

Graduation in Engineering, Math or Statistics and a passion for data analysis

Time Commitment

12 Weeks full time

Format

Virtual Instructor-Led Training

Language

English
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Course Content

Data Science Overview
  • Life Cycle
  • Machine Learning/Big Data
  • Tools
SQL – Database for Data Analytics
  • Environment Creation
  • Connecting with Python
  • Data Types
  • Table Creation
  • SQL Queries
Python Basics
  • Python Advantage
  • Python Installation/Anaconda Installation
  • Basics Of Python
  • Variables
  • Data Types
  • Arithmetic Operators
  • Assignment Operators
  • Comparison Operators
  • Logical Operators
  • Exceptions
  • If Then Else
  • For Loop
  • While Loop
  • Lambda Function
  • Module
Statistics Basics
  • Descriptive Statistics
  • Cumulative Distribution
  • Continuous Distribution
  • Probability
  • Operations on distributions
  • Hypothesis testing
  • Correlation
Data Extraction
  • Data Extraction methods
  • Raw Data/Processed Data
  • Text format to Data frame
  • JSON to Data frame
  • PDF to Data frame
  • Excel to Data frame
  • Web to Data frame
Python EDA
  • Data Cleaning Pandas
  • EDA
  • Data Pre-processing
  • Imbalanced
Data Visualization
  • Types of Graphs
  • Seaborne
  • Matplotlib
  • Plotly
  • Missing no
Machine Learning Algorithms
  • Introduction
  • Process Flow
  • Supervised Learning
  • Linear Regression
  • Logistic Regression
  • Decision Tree
  • Random Forest
  • XGBoost
  • Unsupervised Learning
  • K Means
Data Science Application Lifecycle
  • Data Structure and Tools
  • Live Projects and Use Cases
Natural Language Processing
  • Text Classification Applications
  • Similarity Recommendation App
Chat Bot/ Voice Bot Applications
  • Develop a Live Chat Bot
Data Science Application Lifecycle
  • Data Structure and Tools
  • Live Projects and Use Cases
Basics of Business Intelligence
  • Introduction to Business Intelligence
  • Microsoft Excel Basics
  • Types of Graphs
  • Business Intelligence Tools
Tableau
  • Introduction to Business Intelligence
  • Microsoft Excel Basics
  • Types of Graphs
  • Business Intelligence Tools
Basics of Business Intelligence
  • Introduction to Tableau
  • Microsoft Excel Basics
  • Tableau Advantages
  • Tableau Installation
  • Connecting To Data Bases
  • Data Blending
  • Visual Analytics
  • Reports Dashboards and Stories
  • Punishing Reports
Power BI
  • Introduction to Power BI
  • Power BI Basics
  • Power BI Advantages
  • Installation of Desktop Version
  • Connecting To Data Bases
  • Types of Graphs
  • Data Reports and Colors
  • Data Cleaning
  • Data Modelling
  • Saving BI Reports
Business Intelligence Lifecycle
  • Other BI Tools
  • Live Projects and Use Cases
Basics of Big Data
  • Introduction to Big Data
  • Five V’s of Big Data
  • Source of Big Data
  • Big Data Challenges
Hadoop
  • Introduction to Hadoop
  • Hadoop Architecture
  • Name Node, Data Node, Secondary Node
  • Job tracker, Task Tracker
  • HDFS
  • Map Reduce
  • Hadoop Configuration
PySpark
  • Introduction to PySpark
  • RDD Programming :Overview of Spark basics – RDDs
  • Spark SQL, Datasets, and Data Frames
  • Structured Streaming: Processing structured data streams with relation queries
  • Spark Streaming
  • Applying machine learning algorithms
  • Creating Cluster
  • Data Frame
  • Pipe Line Components
  • Parameters
  • Saving and Loading Pipelines
Big Data Analytics
  • Big Data Life Cycle
  • Executing ML in Big Data
Image Processing Applications – Overview

Why Study With Us?

Trainer Profile Sample

Trainer 1:

Work Experience
Core Technology Faculty
  • 10 + Years of experience in data base administration and cloud technologies
  • Masters in Engineering (Big Data and Cloud Technologies)
  • Worked on Big data environment with real time experience in Hadoop / Map Reduce / Spark / AWS Cloud technologies
  • Developed real time big data solutions for Banking and Finance clients
  • Created high end distributed environment and maintained full project life cycle
  • Developed end to end data center virtualization

Skills
  • Database: Oracle, MSSQL, MySQL, NoSQL
  • Tools: Informatica, Oracle Data Integrator
  • Cloud Technologies: AWS, Microsoft Azure, Google Cloud
  • Big Data Architecture: Hadoop & Spark

Education and Awards
  • Masters in Network Engineering
  • Cloud era Certified Associate (CCA) Data Analyst
  • AWS Certified Big Data – Specialty

Trainer 2:

Work Experience
Core Technology Faculty
  • 15 + Years of experience in 5 continents data analytics team
  • Masters in Software Engineers
  • Worked on building accurate models in predicting the companies’ profits with 98% success rate
  • Provided analytical solutions for business decisions in critical situations
  • Handled end to data analytics life cycle projects of Banking, Insurance, Manufacturing Industries

Skills
  • Database: SQL, Oracle
  • Language: Python, R, PySpark
  • Tools: Tableau, Power BI
  • Other Skills: Statistics, Machine Learning

Education and Awards
  • PhD in Data Science
  • Master in Engineering (Computer Science)
  • IBM Certified Data Analyst

FAQs

Who can join this program?

The minimum requirement is a Bachelor’s degree in engineering and experience in data base management system and cloud technology.

Whether experience is required to join this program?

Yes, a minimum experience of two years in data engineering is required to join this program.

How is the market demand for Big Data Analyst?

The exponential growth of data worldwide has led to companies investing in the management of large databases. To manage Big Data, the combination of Big Data Engineering and Data Analytics is rare but highly effective and rewarding for companies. At present, there is a big skill gap but a huge demand for professionals with Big Data certification.

Will I get any placement support after completing the program?

Yes, you will get placement assistance from NLL Academy.

What is the average salary of a Big Data Analyst?

The average annual compensation of a Big Data Analyst is about $87,000 in USA and between Rs.7, 00,000 and 12, 00,000 in India, depending on the project exposure.

What are the usual job roles available after completing a Big Data Analytics course?

After Big Data Analytics certification, you may become a Data Engineer, Big Data Analytics Software Engineer or a Big Data Specialist.

Contact us now for detailed curriculum and more!

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