Data Analytics Course in Machilipatnam

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Live Online & Classroom Training

Transform your career with our industry-focused Data Analytics Training in Machilipatnam . Gain expertise in data analysis, visualization, and tools like Python, SQL, Excel, and Tableau. Learn to uncover insights, predict trends, and make data-driven decisions. Perfect for beginners and professionals, this course is your gateway to a thriving career in analytics!

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Objectives of Data Analytics Course in Machilipatnam

This course presents a gentle introduction into the concepts of data analysis, the role of a Data Analyst, and the tools that are used to perform daily functions. You will gain an understanding of the data ecosystem and the fundamentals of data analysis, such as data gathering or data mining. You will then learn the soft skills that are required to effectively communicate your data to stakeholders, and how mastering these skills can give you the option to become a data driven decision maker.

This course will help you to differentiate between the roles of a Data Analyst, and Data Engineer. You will learn the responsibilities of a Data Analyst and exactly what data analysis entails. You will be able to summarize the data ecosystem, such as databases and data warehouses. You will then uncover the major vendors within the data ecosystem and explore the various tools on-premise and in the cloud. Continue this exciting journey and discover Big Data platforms such as Hadoop, Hive, and Spark. By the end of this course you will be able to visualize the daily life of a Data Analyst, understand the different career paths that are available for data analytics, and identify the many resources available for mastering this profession.

We Provide you real-time business Intelligence Data Analytics and placement-focused Data Analytics training Our Data Analytics course includes basic to advanced levels and our course is designed to get placement in MNC companies in Machilipatnam and all over India.

“The Data Analyst training in Machilipatnam” in collaboration with IBM will give you expertise in Data Analysis. You can enroll in this Data Analyst course in Machilipatnam led by experts in the industry, and learn the top analytics tools and techniques, the languages of R and Python, and how to work with SQL databases, create data visualizations, and apply predictive analytics and statistics in a business environment.

Ways to Use Data Analytics

Now that you have looked at what data analytics is, let’s understand how we can use data analytics.

Improved Decision Making
Better Customer Service
Efficient Operations
Effective MarketingImproved Decision Making:
Data Analytics eliminates guesswork and manual tasks. Be it choosing the right content, planning marketing campaigns, or developing products. Organizations can use the insights they gain from data analytics to make informed decisions. Thus, leading to better outcomes and customer satisfaction.
Better Customer Service:

Data analytics allows you to tailor customer service according to their needs.It also provides personalization and builds stronger relationships with customers.Analyzed data can reveal information about customers’ interests,concerns, and more.It helps you give better recommendations for products and services.

Efficient Operations:

With the help of data analytics, you can streamline your processes, save money, and boost production. With an improved understanding of what your audience wants, you spend lesser time creating ads and content that aren’t in line with your audience’s interests.

Effective Marketing:

Data analytics gives you valuable insights into how your campaigns are performing. This helps in fine-tuning them for optimal outcomes. Additionally, you can also find potential customers who are most likely to interact with a campaign and convert into leads.

Let’s now dive into the various steps involved in data analytics.

Key Features

Instructor-Led Sessions

50 Hours of Online Live Instructor-Led Classes. Weekend Classes: 24 sessions of 2 hours each. Weekday Classes: 50 sessions of 1 hour each.

Real-Life Case Studies

Live project based on any of the selected use cases, involving implementation of the various concepts.

Assignments

Each class will be followed by practical assignments.

Lifetime Access

You get lifetime access to LMS where presentations, quizzes, installation guide & class recordings.

24 X 7 Expert Support

We have 24×7 online support team to resolve all your technical queries, through ticket based tracking system, for the lifetime.

Certification

After completing your final course project successfully emax will provide you certificate as a SAP Trainee.

Achieve Your Goals With Skill Craft

Skill Craft builds your future with comprehensive coursework and unparalleled placement support.

Data Analytics Course Syllabus

Skill Craft Institute’s Data Analytics Course Syllabus comes with 100% placement support, ensuring students are guaranteed placement in esteemed organizations. The syllabus is meticulously designed with the expertise of leading professionals and industry experts, with extensive hours invested to keep it up-to-date with current trends. Everything our students learn in the course is aligned with the latest data analytics practices, increasing their chances of securing a successful career in the fast-growing field of data analytics.

DATA ANALYSIS FOUNDATION – 6 MODULES

MODULE 1: DATA ANALYSIS FOUNDATION

• Data Analysis Introduction
• Data Preparation for Analysis
• Common Data Problems
• Various Tools for Data Analysis
• Evolution of Analytics domain

MODULE 2: CLASSIFICATION OF ANALYTICS

• Four types of the Analytics
• Descriptive Analytics
• Diagnostics Analytics
• Predictive Analytics
• Prescriptive Analytics
• Human Input in Various type of Analytics

MODULE 3: CRIP-DM Model

• Introduction to CRIP-DM Model
• Business Understanding
• Data Understanding
• Data Preparation
• Modeling, Evaluation, Deploying,Monitoring

MODULE 4: UNIVARIATE DATA ANALYSIS

• Summary statistics -Determines the value’s center and spread.
• Measure of Central Tendencies: Mean, Median and Mode
• Measures of Variability: Range, Interquartile range, Variance and Standard Deviation
• Frequency table -This shows how frequently various values occur.
• Charts -A visual representation of the distribution of values.

MODULE 5: DATA ANALYSIS WITH VISUAL CHARTS

• Line Chart
• Column/Bar Chart
• Waterfall Chart
• Tree Map Chart
• Box Plot

MODULE 6: BI-VARIATE DATA ANALYSIS

• Scatter Plots
• Regression Analysis
• Correlation Coefficients

PYTHON FOUNDATION – 4 MODULES

MODULE 1: PYTHON BASICS

• Introduction of python
• Installation of Python and IDE
• Python Variables
• Python basic data types
• Number & Booleans, strings
• Arithmetic Operators
• Comparison Operators
• Assignment Operators

MODULE 2: PYTHON CONTROL STATEMENTS

• IF Conditional statement
• IF-ELSE
• NESTED IF
• Python Loops basics
• WHILE Statement
• FOR statements
• BREAK and CONTINUE statements

MODULE 3: PYTHON DATA STRUCTURES

• Basic data structure in python
• Basics of List
• List: Object, methods
• Tuple: Object, methods
• Sets: Object, methods
• Dictionary: Object, methods

MODULE 4: PYTHON FUNCTIONS

• Functions basics
• Function Parameter passing
• Lambda functions
• Map, reduce, filter functions

STATISTICS ESSENTIALS – 4 MODULES

MODULE 1 : OVERVIEW OF STATISTICS

Introduction to Statistics
Descriptive And Inferential Statistics
Basic Terms Of Statistics
Types Of Data

MODULE 2 : HARNESSING DATA

Random Sampling
Sampling With Replacement And Without Replacement
Cochran’s Minimum Sample Size
Types of Sampling
Simple Random Sampling
Stratified Random Sampling
Cluster Random Sampling
Systematic Random Sampling
Multi stage Sampling
Sampling Error
Methods Of Collecting Data

MODULE 3 : EXPLORATORY DATA ANALYSIS

Exploratory Data Analysis Introduction
Measures Of Central Tendencies: Mean, Median And Mode
Measures Of Central Tendencies: Range, Variance And Standard Deviation
Data Distribution Plot: Histogram
Normal Distribution & Properties
Z Value / Standard Value
Empherical Rule and Outliers
Central Limit Theorem
Normality Testing
Skewness & Kurtosis
Measures Of Distance: Euclidean, Manhattan And MinkowskiDistance
Covariance & Correlation

MODULE 4 : HYPOTHESIS TESTING

Hypothesis Testing Introduction
P- Value, Critical Region
Types of Hypothesis Testing
Hypothesis Testing Errors : Type I And Type Ii
Two Sample Independent T-test
Two Sample Relation T-test
One Way Anova Test
Application of Hypothesis testing

DATA ANALYSIS ASSOCIATE – 7 MODULES

MODULE 1: COMPARISION AND CORRELATION ANALYSIS

• Data comparison Introduction,
• Performing Comparison Analysis on Data
• Concept of Correlation
• Calculating Correlation with Excel
• Comparison vs Correlation
• Hands-on case study : Comparison Analysis
• Hands-on case study Correlation Analysis

MODULE 2: VARIANCE AND FREQUENCY ANALYSIS

• Variance Analysis Introduction
• Data Preparation for Variance Analysis
• Performing Variance and Frequency Analysis
• Business use cases for Variance Analysis
• Business use cases for Frequency Analysis

MODULE 3: RANKING ANALYSIS

• Introduction to Ranking Analysis
• Data Preparation for Ranking Analysis
• Performing Ranking Analysis with Excel
• Insights for Ranking Analysis
• Hands-on Case Study: Ranking Analysis

MODULE 4: BREAK EVEN ANALYSIS

• Concept of Breakeven Analysis
• Make or Buy Decision with Break Even
• Preparing Data for Breakeven Analysis
• Hands-on Case Study: Manufacturing

MODULE 5: PARETO (80/20 RULE) ANALSYSIS

• Pareto rule Introduction
• Preparation Data for Pareto Analysis,
• Performing Pareto Analysis on Data
• Insights on Optimizing Operations with Pareto Analysis
• Hands-on case study: Pareto Analysis

MODULE 6: Time Series and Trend Analysis

• Introduction to Time Series Data
• Preparing data for Time Series Analysis
• Types of Trends
• Trend Analysis of the Data with Excel
• Insights from Trend Analysis

MODULE 7: DATA ANALYSIS BUSINESS REPORTING

• Management Information System Introduction
• Various Data Reporting formats
• Creating Data Analysis reports as per the requirements

ADVANCED DATA ANALYTICS – 4 MODULES

MODULE 1: DATA ANALYTICS FOUNDATION

• Business Analytics Overview
• Application of Business Analytics
• Benefits of Business Analytics
• Challenges
• Data Sources
• Data Reliability and Validity

MODULE 2: OPTIMIZATION MODELS

• Predictive Analytics with Low Uncertainty;Case Study
• Mathematical Modeling and Decision Modeling
• Product Pricing with Prescriptive Modeling
• Assignment 1 : KERC Inc, Optimum Manufacturing Quantity

MODULE 3: PREDICTIVE ANALYTICS WITH REGRESSION

• Mathematics behind Linear Regression
• Case Study : Sales Promotion Decision with Regression Analysis
• Hands on Regression Modeling in Excel

MODULE 4: DECISION MODELING

• Predictive Analytics with High Uncertainty
• Case Study-Monte Carlo Simulation
• Comparing Decisions in Uncertain Settings
• Trees for Decision Modeling
• Case Study : Supplier Decision Modeling – Kickathlon Sports Retailer

PREDICTIVE ANALYTICS WITH ML – 8 MODULES

MODULE 1: DATA ANALYTICS FOUNDATION

• Business Analytics Overview
• Application of Business Analytics
• Benefits of Business Analytics
• Challenges
• Data Sources
• Data Reliability and Validity

MODULE 2: OPTIMIZATION MODELS

MODULE 3: PREDICTIVE ANALYTICS WITH REGRESSION

• Mathematics behind Linear Regression
• Case Study : Sales Promotion Decision with Regression Analysis
• Hands on Regression Modeling in Excel

MODULE 4: DECISION MODELING

DATABASE: SQL AND MONGODB – 7 MODULES

MODULE 1: DATABASE INTRODUCTION

• DATABASE Overview
• Key concepts of database management
• CRUD Operations
• Relational Database Management System
• RDBMS vs No-SQL (Document DB)

MODULE 2: SQL BASICS

• Introduction to Databases
• Introduction to SQL
• SQL Commands
• MY SQL workbench installation

MODULE 3: DATA TYPES AND CONSTRAINTS

• Numeric, Character, date time data type
• Primary key, Foreign key, Not null
• Unique, Check, default, Auto increment

MODULE 4: DATABASES AND TABLES (MySQL)

• Create database
• Delete database
• Show and use databases
• Create table, Rename table
• Delete table, Delete table records
• Create new table from existing data types
• Insert into, Update records
• Alter table

MODULE 5: SQL JOINS

• Inner join, Outer Join
• Left join, Right Join
• Self Join, Cross join
• Windows Functions: Over, Partition, Rank

MODULE 6: SQL COMMANDS AND CLAUSES

• Select, Select distinct
• Aliases, Where clause
• Relational operators, Logical
• Between, Order by, In
• Like, Limit, null/not null, group by
• Having, Sub queries

MODULE 7: DOCUMENT DB/NO-SQL DB

• Introduction of Document DB
• Document DB vs SQL DB
• Popular Document DBs
• MongoDB basics
• Data format and Key methods
• MongoDB data management

BIG DATA FOUNDATION – 4 MODULES

MODULE 1: BIG DATA INTRODUCTION

• Big Data Overview
• Five Vs of Big Data
• What is Big Data and Hadoop
• Introduction to Hadoop
• Components of Hadoop Ecosystem
• Big Data Analytics Introduction

MODULE 2: HDFS AND MAP REDUCE

• HDFS – Big Data Storage
• Distributed Processing with Map Reduce
• Mapping and reducing stages concepts
• Key Terms: Output Format, Partitioners, Combiners, Shuffle, and Sort

MODULE 3: PYSPARK FOUNDATION

• PySpark Introduction
• Spark Configuration
• Resilient distributed datasets (RDD)
• Working with RDDs in PySpark
• Aggregating Data with Pair RDDs

MODULE 4: SPARK SQL and HADOOP HIVE

• Introducing Spark SQL
• Spark SQL vs Hadoop Hive

BI ANALYST – 4 MODULES

MODULE 1: TABLEAU FUNDAMENTALS

• Introduction to Business Intelligence & Introduction to Tableau
• Interface Tour, Data visualization: Pie chart, Column chart, Bar chart.
• Bar chart, Tree Map, Line Chart
• Area chart, Combination Charts, Map
• Dashboards creation, Quick Filters
• Create Table Calculations
• Create Calculated Fields
• Create Custom Hierarchies

MODULE 2: POWER-BI BASICS

• Power BI Introduction
• Basics Visualizations
• Dashboard Creation
• Basic Data Cleaning
• Basic DAX FUNCTION

MODULE 3: DATA TRANSFORMATION TECHNIQUES

• Exploring Query Editor
• Data Cleansing and Manipulation:
• Creating Our Initial Project File
• Connecting to Our Data Source
• Editing Rows
• Changing Data Types
• Replacing Values

MODULE 4: CONNECTING TO VARIOUS DATA SOURCES

• Connecting to a CSV File
• Connecting to a Webpage
• Extracting Characters
• Splitting and Merging Columns
• Creating Conditional Columns
• Creating Columns from Examples
• Create Data Model

Why should you build your career in Data Analytics in Machilipatnam?

The data analyst role helps an organization make better decisions, be more competitive, or even shape the workforce culture. Because the information you provide is critical to a business’s bottom line, you have the opportunity to be a data-driven leader.

What skills are needed to be a Data Analytics?

A data scientist Analytics should be an expert in multiple skills such as Mathematics, statistics, Big Data.
Data Analysis should have a deeper understanding of Mathematics, Statistics and should have good analytical skills to understand patterns in these data.
We should be able to develop machine learning algorithms that are capable of doing predictive analysis and generate sample data based on given datasets.

Who can enroll in this Data Analytics Certification Training in Machilipatnam?

Following are the individuals who must take up this Data Analytics course in Machilipatnam and up skill themselves:

Marketing Managers
Banking Professionals
Sales Professionals
IT Professionals
Data Analytics Professionals
Supply Chain Network Managers

Training Highlights:

Clean Data with Power Query Understand Import,
Direct Query and Live Modes Understand Schedule Refresh using Data Gateways
Understand Data Modeling Understand Row Level Security
Understand PBI Cloud Service
Deployment Write Complex DAX Create Visualizations
Build Reports and Dashboards

Want to learn with a personalized course curriculum?

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What Will You Learn

A detailed overview of the course, including key topics, objectives, and module sequence.

1 Months

Online

24+ hrs

12+

Placement Support

What is data analytics?

Data analytics involves examining raw data to uncover patterns, trends, and insights that help in decision-making. At Skills Craft Institute, our courses cover the fundamentals of data analytics to help you build a strong foundation.

What is data analytics?

How much data is needed for analytics?

The amount of data required depends on the type of analysis being performed. Even small datasets can provide valuable insights, while larger datasets are used for complex predictions and machine learning models.

What are the 3 components of data analytics?

The three key components of data analytics are:

Descriptive Analytics (what happened)
Predictive Analytics (what might happen)
Prescriptive Analytics (what should be done)

What are the main phases of analytics?

The analytics process includes:

Data Collection
Data Cleaning
Exploratory Data Analysis (EDA)
Model Building & Interpretation
Visualization & Reporting

How much does data analytics cost?

The cost of data analytics depends on factors like tools, software, and expertise level. At Skills Craft Institute, we offer affordable courses to help learners master data analytics without financial strain.

How to become a data analyst?

To become a data analyst, you need to learn data manipulation, statistical analysis, and visualization techniques. Skills Craft Institute provides hands-on training in these areas, helping you develop job-ready skills.

What do data analysts do all day?

Data analysts spend their time collecting, cleaning, analyzing, and interpreting data, creating reports, and using visualization tools to communicate insights effectively.

What tools and skills do I need to become a data analyst?

You need proficiency in tools like Excel, SQL, Python, R, Tableau, and Power BI. Courses at Skills Craft Institute focus on equipping students with these essential tools.

What course should I choose to become a data analyst?

Look for courses that cover statistics, SQL, Python, data visualization, and machine learning. Skills Craft Institute offers a well-structured data analytics program designed for beginners.

Is data analysis a good career?

Yes! Data analysis is a highly in-demand field with competitive salaries and growth opportunities. At Skills Craft Institute, we help students transition into this rewarding career.

What is the roadmap to becoming a data analyst?

A typical roadmap includes:

Learning data analysis fundamentals
Gaining hands-on experience with tools
Working on real-world projects
Building a strong portfolio

Can I become a data analyst if I’m from a non-technical background?

Yes! Many successful data analysts come from non-technical backgrounds. Skills Craft Institute offers beginner-friendly courses to help anyone enter the field.

What are the common problems data analysts encounter?

Challenges include data quality issues, missing values, misleading insights, and handling large datasets.

Which technical tools have you used for analysis and presentation?

Common tools include Excel, SQL, Python, R, Tableau, Power BI, and Google Data Studio.

What is the significance of Exploratory Data Analysis (EDA)?

EDA helps in understanding data patterns, identifying anomalies, and preparing data for advanced analysis.

What are the different types of sampling techniques used by data analysts?

Sampling techniques include random sampling, stratified sampling, and cluster sampling.

What are your strengths and weaknesses as a data analyst?

Strengths may include problem-solving and attention to detail, while weaknesses could be learning new tools or handling large datasets.

What are the ethical considerations of data analysis?

Ethical concerns include data privacy, bias in algorithms, and transparency in decision-making.

What are some common data visualization tools you have used?

Popular tools include Tableau, Power BI, Matplotlib, Seaborn, and Google Data Studio.