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Data Science Course With Placement

Learn Data Science Course at Anubhav with Industry Expert Trainer.

Anubhav Computer Institute is recognized as the Best Institute for Data Science course in Chembur, Govandi,  & Chembur Naka.

Get Free Demo Class

Review 4.8/5 (1878)

Top Data Science Course in Mumbai

0 +

Student Enrolled

0 4.8

Google reviews

0 6 Months

Duration

0 240 Hours

Of Learning

0 Job Rediness

Certificate Program in DATA SCIENCE

Industry Expert Trainer

Train with courses designed & delivered by industry experts.

Live Practical Traininig

100% Practical Training

Updated Syllabus & Tools

Learn all the Modules of Microsoft Office course practically

Recognized Certificates

Industry Recognized Certification to Add Value to Your Resume.
Online Data Science Course
Offline Data Science Course

Why Should You Learn Data Science?

  1. High Demand for Data Professionals: Companies are increasingly relying on data-driven insights to make strategic decisions, driving the need for skilled professionals who can interpret and analyze data effectively.
  2. Versatility and Flexibility: Data science skills are versatile and applicable across a wide range of industries and domains.
  3. Practical Application: Data science courses typically offer hands-on experience with real-world datasets and projects.
  4. Global Recognition: Data science is a globally recognized field with opportunities for employment worldwide. By earning a certification or completing a data science course from a reputable institution, you’ll gain recognition for your skills and expertise on a global scale.
Get Free Demo Class

DATA SCIENCE Course Syllabus

  • Installation of R & R Studio
  • Features of R
  • Variables in R
  • Constants in R
  • Operators in R
  • Datatypes and R Objects
  • Accepting Input from keyboard
  • Important Built-in functions
  • Creating Vectors
  • Accessing elements of a Vector
  • Operations on Vectors
  • Vector Arithmetic
  • if statement
  • if…else statement
  • Switch () function
  • repeat loop
  • while loop
  • for loop
  • break statement
  • next statement
  • Formal and Actual arguments
  • Named arguments
  • Global and local variables
  • Argument and lazy evaluation of functions
  • Recursive functions
  • Creating matrices
  • Accessing elements of a Matrix
  • Operations on Matrices
  • Matrix transpose
  • Creating strings
  • paste() and paste0()
  • Formatting numbers and string using format()
  • String manipulation
  • Creating lists
  • Manipulating list elements
  • Merging lists
  • Converting lists to vectors
  • Creating arrays
  • Accessing array elements
  • Calculations across array elements
  • Understanding factors
  • Modifying factors
  • Factors in Data frames
  • 10. DATA FRAMES IN R
  • Creating data frame
  • Operations on data frames
  • Accessing data frames
  • Creating data frames from various sources
  • Need for data visualization
  • Plotting categorical data
  • Stacked bar plot
  • Histogram
  • plot() function and line plot
  • pie chart
  • Scatter plot
  • Box plot
  • Important functions in stringr
  • Load data into dataframe
  • Viewing the data
  • Selecting columns
  • Selecting rows
  • Reordering the rows
  • Pipe operator
  • Group operations
  • What is NumPy array?
  • Array Constructor
  • Introduction to Array
  • Range() function
  • How to create 2-D Arrays
  • Matrix Operation
  • What is Array indexing and Slicing?
  • Indexing in 1-D Arrays
  • Indexing in 2-D Arrays
  • Slicing in 1-D Arrays
  • Slicing in 2-D Arrays
  • Array Comparison
  • Introduction to pandas
  • Pandas and Data Manipulation
  • What is Labeled and structured data?
  • What are Series and DataFrame objects?
  • What is Data Cleansing?
  • What is Data visualization?
  • Deleting and Dropping Columns
  • Series
  • Apply() function
  • Creating Series
  • Data Frame and Basic Functionality
  • Head() function
  • About: Merges and Joins
  • What is Data fill?
  • Mean() function
  • Data Frame Manipulation
  • Indexing and missing Values
  • Grouping and Reshaping
  • From excel
  • From CSV
  • DAX Introduction
  • CALCULATE – SUMIF
  • CALCULATE – SUMIFS
  • LOOKUPVALUE
  • CALENDAR, FORMAT, LEFT, MONTH, YEAR, DAY
  • DATEDIF, EDATE, NOW, QUARTER
  • FILTER, FILTERS, DISTINCT, ALLEXPECT
  • CONTAINS, ISBLANK, ISTEXT, ISNONTEXT, ISNUMBER
  • IF, Nested IF OR, AND
  • SUMX
  • at and iat
  • loc() Function and Iloc() function
  • head() Function and tail() Function
  • About describe() function
  • groupby() function
  • crosstab() function
  • How to combine Data Frames?
  • How to add and remove rows and columns?
  • How to sort data?
  • How to handle missing values?
  • How to handle duplicates?
  • How to handle Date and Time?
  • Processing and Cleaning Data through Pandas methods
  • Dealing with missing values
  • Introduction to Data Visualization
  • Matplotlib package:
  • Introduction to MatPlotlib Library
  • How to use matplotlib.pyplot interface
  • Types of charts
  • How to plot Histogram and pie chart?
  • About: Bar Chart, Stacked Chart, Scatter plot
  • DML (Data Manipulation Language), DDL (Data Definition Language), DQL
  • (Data Query Language)
  • How to create, alter and drop the DDL?
  • How to insert, update, delete and merge the DML?
  • How to select the DQL?
  • Primary and foreign key,unique key
  • How to select distinct?
  • Addition (+)
  • Subtraction (-)
  • Multiplication (*)
  • Division (/)
  • Modulus (%)
  • =
  • !=
  • <>
  • >
  • <
  • >=
  • <=
  • AND
  • OR
  • BETWEEN
  • SQL like, where
  • order by,
  • view, joins, aliases
  • Inner Join
  • Full (Outer) Join
  • Left (Outer) Join
  • Right (Outer) Join
    1. String Functions:
    • Char_length
    • Lower
    • Reverse
    • Upper
    1. Numeric Functions:
    • Max
    • Min
    • Sum
    • Avg
    • Count
    1. Date Functions:
    • Curdate
    • Curtime
    • Now
    • Month
    • Year
    • Day
    • Extract
    • Hour
    • Minute
    • Second
  • What is Power BI
  • How to get Power BI
  • The Parts of Power BI
  • What will you learn in this course
  • When we should use Power BI
  • Core Blocks of Power BI
  • Power BI Desktop vs Pro Version
  • Power BI Menus and Options
  • Menus and Options
  • Power BI components
  • Types of Data connection
  • How to Connect to Data Sources
  • Change data source
  • How to Import Data (Excel Report)
  • How to Import Data (Excel Table)
  • How to Import Data (Multiple Excel Table)
  • How to Import Data (Multiple Table and Sheets)
  • How to Import Data (Multiple Excel Reports)
  • How to Import Data (Multiple Excel Reports Multiple Sheets)
  • Creating Tables in Power BI Apply () function
  • How to Insert TextBox and Image
  • Data Frame and Basic Functionality
  • Data Cleansing
  • Data Transformation and Cleansing Intro
  • Data Transformation and Cleansing – Home
  • Data Transformation and Cleansing – Transform
  • Data Transformation and Cleansing – Column
  • Data Transformation and Cleansing – View
  • Data Transformation and Append Queries
  • Data Transformation and Merge
  • Relationship Basic
  • How to create Relationship and Why
  • Many to One Relationship
  • One to One Relationship
  • One to Many Relationship
  • Many to Many Relationship
  • Chart and Visualisation Basic
  • Data Import and Basic Check and Card Creation
  • Table and Matrix
  • Line Chart
  • Stacked Column Chart
  • Donut Chart Month
  • 100% Stacked Column and Bar Chart
  • Commentaries Box
  • Slicer
  • Publish Dashboard and how to do Analysis
  • Dashboard 2 – Intro
  • Actual vs Budget Data Load
  • Title and Multi Card
  • Matrix
  • Monthly Trend Actual vs Budget
  • Waterfall Chart
  • Pie Chart
  • Gauge Chart
  •  Map Chart
  • Analysis and Keynote 1
  • Time Slicer
  • Publish Dashboard
  • Create Bookmark and Add Button
  • Visual, Page, all Page Filter
  • Tooltips
  • Drill Through
  • Selection Panel
  • Funnel, Area, Ribbon Chart
  • Dynamic Commentaries
  • Visuals and Others Summary
  • Relationship Model Challenges
  • Quick Measures
  • Publish Report
  • Pinning Visuals
  • Export Data and Visuals – Excel, PPT and PDF Format
Data Science Syllabus

Now Priced at Just

Rs.50,000/-

Duration: 6 Months

Limited Seats

About Data Science

Data science is an interdisciplinary field that involves extracting insights and knowledge from structured and unstructured data. It encompasses various techniques, including data analysis, machine learning, statistical modelling, and data visualization, to uncover patterns, trends, and correlations within data sets. In today’s data-driven world, data science plays a crucial role in informing decision-making processes, optimizing business operations, and driving innovation across industries.

Enroll Now

Our Location Center

Anubhav Data Science Class Near Chembur Station (Head Office)

Anubhav Data Science Class Near Govandi Station

Anubhav Data Science Class Near Chembur Naka

Frequently asked questions For Data Science

Our training center is conveniently located in Chembur, near Govandi and Chembur Naka.

You can enroll by visiting our website or contacting our office directly. Also Contact Directly Whatsapp No. +91 9167243836 Phone No. +91 9167243836

You can enroll by visiting our website or contacting our office directly. Also Contact Directly Whatsapp No. +91 9167243836 Phone No. +91 9167243836

Yes, we offer both online and offline classes to cater to different preferences. You can choose the mode that suits your schedule and learning style.

Yes, a certificate is provided upon course completion, and placement assistance is offered to help you secure relevant job opportunities.

The course fee is 50,000/- for the entire program.

The course duration is 6 months, providing a condensed and intensive learning experience.

Data Science Course with Certification and
100% Placement Assistance

This Data Science certification course will give you hands-on development experience and prepare you for an exciting career as a professional Data Science. Training Course will teach you the Data Science with the certification 100% guarantee placement.

View all Courses
Enroll in The Best training Program and Give Your Career Amazing Boost
Our Alumnis Work Here

Data Science Course With Placement

Learn Data Science Course at Anubhav with Industry Expert Trainer.

Anubhav Computer Institute is recognized as the Best Institute for Data Science course in Chembur, Govandi,  & Chembur Naka.

Get Free Demo Class

Review 4.8/5 (1878)

Top Data Science Course in Mumbai

0 +

Student Enrolled

0 4.8

Google reviews

0 6 Months

Duration

0 240 Hours

Of Learning

0 Job Rediness

Certificate Program in DATA SCIENCE

Industry Expert Trainer

Train with courses designed & delivered by industry experts.

Live Practical Traininig

100% Practical Training

Updated Syllabus & Tools

Learn all the Modules of Microsoft Office course practically

Recognized Certificates

Industry Recognized Certification to Add Value to Your Resume.
Online Data Science Course
Offline Data Science Course

Why Should You Learn Data Science?

  1. High Demand for Data Professionals: Companies are increasingly relying on data-driven insights to make strategic decisions, driving the need for skilled professionals who can interpret and analyze data effectively.
  2. Versatility and Flexibility: Data science skills are versatile and applicable across a wide range of industries and domains.
  3. Practical Application: Data science courses typically offer hands-on experience with real-world datasets and projects.
  4. Global Recognition: Data science is a globally recognized field with opportunities for employment worldwide. By earning a certification or completing a data science course from a reputable institution, you’ll gain recognition for your skills and expertise on a global scale.
Get Free Demo Class

DATA SCIENCE Course Syllabus

  • Installation of R & R Studio
  • Features of R
  • Variables in R
  • Constants in R
  • Operators in R
  • Datatypes and R Objects
  • Accepting Input from keyboard
  • Important Built-in functions
  • Creating Vectors
  • Accessing elements of a Vector
  • Operations on Vectors
  • Vector Arithmetic
  • if statement
  • if…else statement
  • Switch () function
  • repeat loop
  • while loop
  • for loop
  • break statement
  • next statement
  • Formal and Actual arguments
  • Named arguments
  • Global and local variables
  • Argument and lazy evaluation of functions
  • Recursive functions
  • Creating matrices
  • Accessing elements of a Matrix
  • Operations on Matrices
  • Matrix transpose
  • Creating strings
  • paste() and paste0()
  • Formatting numbers and string using format()
  • String manipulation
  • Creating lists
  • Manipulating list elements
  • Merging lists
  • Converting lists to vectors
  • Creating arrays
  • Accessing array elements
  • Calculations across array elements
  • Understanding factors
  • Modifying factors
  • Factors in Data frames
  • 10. DATA FRAMES IN R
  • Creating data frame
  • Operations on data frames
  • Accessing data frames
  • Creating data frames from various sources
  • Need for data visualization
  • Plotting categorical data
  • Stacked bar plot
  • Histogram
  • plot() function and line plot
  • pie chart
  • Scatter plot
  • Box plot
  • Important functions in stringr
  • Load data into dataframe
  • Viewing the data
  • Selecting columns
  • Selecting rows
  • Reordering the rows
  • Pipe operator
  • Group operations
  • What is NumPy array?
  • Array Constructor
  • Introduction to Array
  • Range() function
  • How to create 2-D Arrays
  • Matrix Operation
  • What is Array indexing and Slicing?
  • Indexing in 1-D Arrays
  • Indexing in 2-D Arrays
  • Slicing in 1-D Arrays
  • Slicing in 2-D Arrays
  • Array Comparison
  • Introduction to pandas
  • Pandas and Data Manipulation
  • What is Labeled and structured data?
  • What are Series and DataFrame objects?
  • What is Data Cleansing?
  • What is Data visualization?
  • Deleting and Dropping Columns
  • Series
  • Apply() function
  • Creating Series
  • Data Frame and Basic Functionality
  • Head() function
  • About: Merges and Joins
  • What is Data fill?
  • Mean() function
  • Data Frame Manipulation
  • Indexing and missing Values
  • Grouping and Reshaping
  • From excel
  • From CSV
  • DAX Introduction
  • CALCULATE – SUMIF
  • CALCULATE – SUMIFS
  • LOOKUPVALUE
  • CALENDAR, FORMAT, LEFT, MONTH, YEAR, DAY
  • DATEDIF, EDATE, NOW, QUARTER
  • FILTER, FILTERS, DISTINCT, ALLEXPECT
  • CONTAINS, ISBLANK, ISTEXT, ISNONTEXT, ISNUMBER
  • IF, Nested IF OR, AND
  • SUMX
  • at and iat
  • loc() Function and Iloc() function
  • head() Function and tail() Function
  • About describe() function
  • groupby() function
  • crosstab() function
  • How to combine Data Frames?
  • How to add and remove rows and columns?
  • How to sort data?
  • How to handle missing values?
  • How to handle duplicates?
  • How to handle Date and Time?
  • Processing and Cleaning Data through Pandas methods
  • Dealing with missing values
  • Introduction to Data Visualization
  • Matplotlib package:
  • Introduction to MatPlotlib Library
  • How to use matplotlib.pyplot interface
  • Types of charts
  • How to plot Histogram and pie chart?
  • About: Bar Chart, Stacked Chart, Scatter plot
  • DML (Data Manipulation Language), DDL (Data Definition Language), DQL
  • (Data Query Language)
  • How to create, alter and drop the DDL?
  • How to insert, update, delete and merge the DML?
  • How to select the DQL?
  • Primary and foreign key,unique key
  • How to select distinct?
  • Addition (+)
  • Subtraction (-)
  • Multiplication (*)
  • Division (/)
  • Modulus (%)
  • =
  • !=
  • <>
  • >
  • <
  • >=
  • <=
  • AND
  • OR
  • BETWEEN
  • SQL like, where
  • order by,
  • view, joins, aliases
  • Inner Join
  • Full (Outer) Join
  • Left (Outer) Join
  • Right (Outer) Join
    1. String Functions:
    • Char_length
    • Lower
    • Reverse
    • Upper
    1. Numeric Functions:
    • Max
    • Min
    • Sum
    • Avg
    • Count
    1. Date Functions:
    • Curdate
    • Curtime
    • Now
    • Month
    • Year
    • Day
    • Extract
    • Hour
    • Minute
    • Second
  • What is Power BI
  • How to get Power BI
  • The Parts of Power BI
  • What will you learn in this course
  • When we should use Power BI
  • Core Blocks of Power BI
  • Power BI Desktop vs Pro Version
  • Power BI Menus and Options
  • Menus and Options
  • Power BI components
  • Types of Data connection
  • How to Connect to Data Sources
  • Change data source
  • How to Import Data (Excel Report)
  • How to Import Data (Excel Table)
  • How to Import Data (Multiple Excel Table)
  • How to Import Data (Multiple Table and Sheets)
  • How to Import Data (Multiple Excel Reports)
  • How to Import Data (Multiple Excel Reports Multiple Sheets)
  • Creating Tables in Power BI Apply () function
  • How to Insert TextBox and Image
  • Data Frame and Basic Functionality
  • Data Cleansing
  • Data Transformation and Cleansing Intro
  • Data Transformation and Cleansing – Home
  • Data Transformation and Cleansing – Transform
  • Data Transformation and Cleansing – Column
  • Data Transformation and Cleansing – View
  • Data Transformation and Append Queries
  • Data Transformation and Merge
  • Relationship Basic
  • How to create Relationship and Why
  • Many to One Relationship
  • One to One Relationship
  • One to Many Relationship
  • Many to Many Relationship
  • Chart and Visualisation Basic
  • Data Import and Basic Check and Card Creation
  • Table and Matrix
  • Line Chart
  • Stacked Column Chart
  • Donut Chart Month
  • 100% Stacked Column and Bar Chart
  • Commentaries Box
  • Slicer
  • Publish Dashboard and how to do Analysis
  • Dashboard 2 – Intro
  • Actual vs Budget Data Load
  • Title and Multi Card
  • Matrix
  • Monthly Trend Actual vs Budget
  • Waterfall Chart
  • Pie Chart
  • Gauge Chart
  •  Map Chart
  • Analysis and Keynote 1
  • Time Slicer
  • Publish Dashboard
  • Create Bookmark and Add Button
  • Visual, Page, all Page Filter
  • Tooltips
  • Drill Through
  • Selection Panel
  • Funnel, Area, Ribbon Chart
  • Dynamic Commentaries
  • Visuals and Others Summary
  • Relationship Model Challenges
  • Quick Measures
  • Publish Report
  • Pinning Visuals
  • Export Data and Visuals – Excel, PPT and PDF Format
Data Science Syllabus

Now Priced at Just

Rs.50,000/-

Duration: 6 Months

Limited Seats

About Data Science

Data science is an interdisciplinary field that involves extracting insights and knowledge from structured and unstructured data. It encompasses various techniques, including data analysis, machine learning, statistical modelling, and data visualization, to uncover patterns, trends, and correlations within data sets. In today’s data-driven world, data science plays a crucial role in informing decision-making processes, optimizing business operations, and driving innovation across industries.

Enroll Now

Our Location Center

Anubhav Data Science Class Near Chembur Station (Head Office)

Anubhav Data Science Class Near Govandi Station

Anubhav Data Science Class Near Chembur Naka

Frequently asked questions For Data Science

Our training center is conveniently located in Chembur, near Govandi and Chembur Naka.

You can enroll by visiting our website or contacting our office directly. Also Contact Directly Whatsapp No.  +91 91672 43835 Phone No. +91 9167243835

Yes, we offer both online and offline classes to cater to different preferences. You can choose the mode that suits your schedule and learning style.

Yes, a certificate is provided upon course completion, and placement assistance is offered to help you secure relevant job opportunities.

The course fee is 50,000/- for the entire program.

The course duration is 6 months, providing a condensed and intensive learning experience.

Data Science Course with Certification and
100% Placement Assistance

This Data Science certification course will give you hands-on development experience and prepare you for an exciting career as a professional Data Science. Training Course will teach you the Data Science with the certification 100% guarantee placement.

View all Courses
Enroll in The Best training Program and Give Your Career Amazing Boost
Our Alumnis Work Here

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