Data Science Course Overview


The success of KVCH's Data Science training program is quantifiable via the provision of specialised instruction and focused study, with an emphasis on the development of individual abilities rather than the dissemination of generalised information. Improve your employability by enrolling in the Data Science training Program at KVCH. Besides helping participants find jobs, this programme provides intensive training sessions. The Data Science Training certification is our most comprehensive curriculum, and it is geared at helping students acquire the knowledge and abilities necessary to get employment as data scientists. As a student, you can study the ins and outs of data science by working on state-of-the-art use cases and projects and then applying what you've learned in the real world.

This data science certification program will help you get started in your chosen area by teaching you the foundations of the discipline and providing you enough practise applying what you learn in real-world scenarios. Work experience using data science tools is highly valued by employers.

Upon successful completion of this data science course, you will get a certificate from KVCH validating your knowledge of cutting-edge data science methods and establishing you as a data science specialist within your organisation.

You will become a highly competent Data scientist after completing this course, since you will learn the following abilities:

  • Data science: What it is and how it works
  • Tendencies in the Future of Data science
  • Advanced Statistics
  • Data Visualisation using Tableau
  • Data Visualisation and Analysis
Machine Learning admin training

When you enrol in our Data science Training Program, you can master the following:

  • Prove that you are versed in the methods of statistical analysis of data used in commercial settings.
  • Incorporate Data Science techniques into your company's issue solving processes.
  • Data mining tools can be put to good use in the real world.
  • Analyse Big Data with the help of state-of-the-art equipment and methods.
  • Use computation logic to create intelligent machines.
  • Apply concepts of teamwork, leadership, decision making, and organisational theory in practical contexts.

You can advance your career in several directions after completing a data science course. Professionals with data science skills may choose from a broad range of career paths. If you have relevant work experience, a data scientist role might provide competitive compensation. The following are some potential professions for you to consider:

  • Data Engineer.
  • Business Intelligence Analyst.
  • Marketing Analyst.
  • Data Architect.
  • Machine Learning Engineer.
  • Database Administrator.

  • LinkedIn reports that the average pay for a data scientist is ₹8,50,000.
  • With 5–8 years of experience, a data scientist may make an average of 10,00,000.
  • Data scientists with one to two years of experience may expect an annual salary of around ₹611,000.
  • As a Data science engineer, you can expect to make a salary of 9,76,133 PA.
  • Data Analyst: 6,02,784 PA
  • A Data Scientist's Annual Salary in India Is ₹ 8,00,000 PA

With the help of Data Science, businesses can quickly and accurately analyse massive amounts of information from a variety of sources to get actionable insights for making more informed choices. Marketing, healthcare, banking, finance, policymaking, and many more fields can all benefit from the use of data science.


  • Accenture. 63,206 data employees
  • Tata Consultancy Services.
  • IBM. 35,986 data employeesCitigroup
  • Deloitte
  • Infosys
  • JPMorgan Chase
  • EY

When you've developed your data science abilities and amassed significant experience, you can branch out into related fields like marketing and sales, or data quality, finance, and business intelligence, or even take on a consulting role with an industry leader that relies heavily on data.

  • You will learn how to use data analytics to manage and assess vast and complicated datasets, as well as to identify problems and provide solutions.
  • Data analytics, which involves gathering and arranging data for the purposes of analysis and prediction, is essential for making important business choices and implementing necessary reforms.
  • A student of this Data Science Training course will have the skills to mine data in a variety of ways that may contribute to the success of their employer.
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Data Science Curriculum


Data Science is a powerful analytics platform to make discoveries. By using different aspects of computer science, data visualisations, data analytics, statistics, R and Python Programming in data science, you may convert voluminous data into meaningful contents.

Topics:
  • Python Statistics for Data Science
  • Databases – MySQL and SQL Queries
  • Data Science Master’s Program
  • Machine Learning
  • Deep Learning
  • Power BI
  • Hadoop- Apache Spark and Scala Certification
  • Tableau - For Data Visualization
  • Data Science Masters - Live Projects
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Introduction To Python
  • Installation and Working with Python
  • Understanding Python variables
  • Python basic Operators
  • Understanding the Python blocks.
Introduction To Variables
  • Variables, expression condition and function
  • Global and Local Variables in Python
  • Packing and Unpacking Arguments
  • Type Casting in Python
  • Byte objects vs. string in Python
  • Variable Scope
Python Data Type
  • Declaring and usingNumeric data types
  • Using stringdata type and string operations
  • Understanding Non-numeric data types
  • Understanding the concept of Casting and Boolean.
  • Strings
  • List
  • Tuples
  • Dictionary
  • Sets
Introduction Keywords and Identifiers and Operators
  • Python Keyword and Identifiers
  • Python Comments, Multiline Comments.
  • Python Indentation
  • Understating the concepts of Operators
List
  • What is List.
  • List Creation
  • List Length
  • List Append
  • List Insert
  • List Remove
  • List Append & Extend using “+” and Keyword
  • List Delete
  • List related Keyword in Python
  • List Revers
  • List Sorting
  • List having Multiple Reference
  • String Split to create a List
  • List Indexing
  • List Slicing
  • List count and Looping
  • List Comprehension and Nested Comprehension
    • Dictionary
      • Dict Creation
      • Dict Access (Accessing Dict Values)
      • Dict Get Method
      • Dict Add or Modify Elements
      • Dict Copy
      • Dict From Keys.
      • Dict Items
      • Dict Keys (Updating, Removing and Iterating)
      • Dict Values
      • Dict Comprehension
      • Default Dictionaries
      • Ordered Dictionaries
      • Looping Dictionaries
      • Dict useful methods (Pop, Pop Item, Str , Update etc.)

      Sets, Tuples and Looping Programming

      Sets
      • What is Set
      • Set Creation
      • Add element to a Set
      • Remove elements from a Set
      • PythonSet Operations
      • Frozen Sets
      Tuple
      • What is Tuple
      • Tuple Creation
      • Accessing Elements in Tuple
      • Changinga Tuple
      • TupleDeletion
      • Tuple Count
      • Tuple Index
      • TupleMembership
      • TupleBuilt in Function (Length, Sort)
      Control Flow
      • Loops
      • Loops and Control Statements (Continue, Break and Pass).
      • Looping techniques in Python
      • How to use Range function in Loop
      • Programs for printing Patterns in Python
      • How to use if and else with Loop
      • Use of Switch Function in Loop
      • Elegant way of Python Iteration
      • Generator in Python
      • How to use nested IF and Else in Python
      • How to use nested Loop in Python
      • Use If and Else in for and While Loop
      • Examples of Looping with Break and Continue Statements
      • How to use IN or NOTkeywordin Python Loop.

      Exception and File Handling, Module, Function and Packages

      Python Exception Handling
      • Python Errors and Built-in-Exceptions
      • Exception handing Try, Except and Finally
      • Catching Exceptions in Python
      • Catching Specific Exception in Python
      • Raising Exception
      • Try and Finally
      Python File Handling
      • Opening a File
      • Python File Modes
      • Closing File
      • Writing to a File
      • Reading from a File
      • Renaming and Deleting Files in Python
      • Python Directory and File Management
      • List Directories and Files
      • Making New Directory
      • Changing Directory
      Python Function, Modules and Packages
      • Python Syntax
      • Function Call
      • Return Statement
      • Write an Empty Function in Python –pass statement.
      • Lamda/ Anonymous Function
      • *argsand **kwargs
      • Help function in Python
      • Scope and Life Time of Variable in Python Function
      • Nested Loop in Python Function
      • Recursive Function and Its Advantage and Disadvantage
      • Organizing python codes using functions
      • Organizing python projects into modules
      • Importing own module as well as external modules
      • Understanding Packages
      • Programming using functions, modules & external packages
      • Map, Filter and Reduce function with Lambda Function
      • More example of Python Function
      • Data Automation (Excel, SQL, PDF etc)

        Python Object Oriented Programming—Oops
        • Concept of Class, Object and Instances
        • Constructor, Class attributes and Destructors
        • Real time use of class in live projects
        • Inheritance, Overlapping and Overloading operators
        • Adding and retrieving dynamic attributes of classes
        • Programming using Oops support
        Python Database Interaction
        • SQL Database connection using
        • Creating and searching tables
        • Reading and Storing configinformation on database
        • Programming using database connections
        Reading an excel
        • Reading an excel file usingPython
        • Writing toan excel sheet using Python
        • Python| Reading an excel file
        • Python | Writing an excel file
        • Adjusting Rows and Column using Python
        • ArithmeticOperation in Excel file.
        • Plotting Pie Charts
        • Plotting Area Charts
        • Plotting Bar or Column Charts using Python.
        • Plotting Doughnut Chartslusing Python.
        • Consolidationof Excel File using Python
        • Split of Excel File Using Python.
        • Play with Workbook, Sheets and Cells in Excel using Python
        • Creating and Removing Sheets
        • Formatting the Excel File Data
        • More example of Python Function
        Working with PDF and MS Word using Python
        • Extracting Text from PDFs
        • Creating PDFs
        • Copy Pages
        • Split PDF
        • Combining pages from many PDFs
        • Rotating PDF’s Pages
        Complete Understanding of OS Module of Python
        • Check Dirs. (exist or not)
        • How to split path and extension
        • How to get user profile detail
        • Get the path of Desktop, Documents, Downloads etc.
        • Handle the File System Organization using OS
        • How to get any files and folder’s details using OS

        Data Analysis & Visualization

        Pandas
        • Read data from Excel File using Pandas More Plotting, Date Time Indexing and writing to files
        • How to get record specific records Using Pandas Adding & Resetting Columns, Mapping with function
        • Using the Excel File class to read multiple sheets More Mapping, Filling Nonvalue’s
        • Exploring the Data Plotting, Correlations, and Histograms
        • Getting statistical information about the data Analysis Concepts, Handle the None Values
        • Reading files with no header and skipping records Cumulative Sums and Value Counts, Ranking etc
        • Reading a subset of columns Data Maintenance, Adding/Removing Cols and Rows
        • Applying formulas on the columns Basic Grouping, Concepts of Aggregate Function
        • Complete Understanding of Pivot Table Data Slicing using iLocand Locproperty (Setting Indices)
        • Under sting the Properties of Pivot Table in Pandas Advanced Reading CSVs/HTML, Binning, Categorical Data
        • Exporting the results to Excel Joins:
        • Python | Pandas Data Frame Inner Join
        • Under sting the properties of Data Frame Left Join (Left Outer Join)
        • Indexing and Selecting Data with Pandas Right Join (Right Outer Join)
        • Pandas | Merging, Joining and Concatenating Full Join (Full Outer Join)
        • Pandas | Find Missing Data and Fill and Drop NA Appending DataFrameand Data
        • Pandas | How to Group Data How to apply Lambda / Function on Data Frame
        • Other Very Useful concepts of Pandas in Python Data Time Property in Pandas (More and More)
        NumPy
        • Introduction to NumPy: Numerical Python
        • Importing NumPy and Its Properties
        • NumPy Arrays
        • Creating an Array from a CSV
        • Operations an Array from aCSV
        • Operations with NumPy Arrays
        • Two-Dimensional Array
        • Selecting Elements from 1-D Array
        • Selecting Elements from 2-D Array
        • Logical Operation with Arrays
        • Indexing NumPy elements using conditionals
        • NumPy’sMean and Axis
        • NumPy’sMode, Median and Sum Function
        • NumPy’sSort Function and More
        MatPlotLib
        • Bar Chart using Python MatPlotLib
        • Column Chart using Python MatPlotLib
        • Pie Chart using Python MatPlotLib
        • Area Chart using Python MatPlotLib
        • Scatter Plot Chart using Python MatPlotLib
        • Play with Charts Properties Using MatPlotLib
        • Export the Chart as Image
        • Understanding plt. subplots () notation
        • Legend Alignment of Chart using MatPlotLib
        • Create Charts as Image
        • Other Useful Properties of Charts.
        • Complete Understanding of Histograms
        • Plotting Different Charts, Labels, and Labels Alignment etc.
        Introduction to Seaborn
        • Introduction to Seaborn
        • Making a scatter plot with lists
        • Making a count plot with a list
        • Using Pandas with seaborn
        • Tidy vs Untidy data
        • Making a count plot with a Dataframe
        • Adding a third variable with hue
        • Hue and scattera plots
        • Hue and count plots
        Visualizing Two Quantitative Variables
        • Introduction to relational plots and subplots
        • Creating subplots with col and row
        • Customizing scatters plots
        • Changing the size of scatter plot points
        • Changing the style of scatter plot points
        • Introduction to line plots
        • Interpreting line plots
        • Visualizing standard deviation with line plots
        • Plotting subgroups in line plots
        Visualizing a Categorical and a Quantitative Variable
        • Current plots and bar plots
        • Count plots
        • Bar plot with percentages
        • Customizing bar plots
        • Box plots
        • Create and interpret a box plot
        • Omitting outliers
        • Adjusting the whiskers
        • Point plots
        • Customizing points plots
        • Point plot with subgroups
        Customizing Seaborn Plots
        • Changing plot style and colour
        • Changing style and palette
        • Changing the scale
        • Using a custom palette
        • Adding titles and labels: Part 1
        • Face Grids vs. Axes Subplots
        • Adding a title to a face Grid object
        • Adding title and labels: Part 2
        • Adding a title and axis labels
        • Rotating x-tics labels
        • Putting it all together
        • Box plot with subgroups
        • Bar plot with subgroups and subplots
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Python - MySQL
  • Introduction to MySQL
  • What is the MySQLdb
  • How do I Install MySQLdb
  • Connecting to the MYSQL
  • Selecting a database
  • Adding data to a table
  • Executing multiple queries
  • Exporting and Importing data tables.
SQL Functions
  • Single Row Functions
  • Character Functions, Number Function, Round, Truncate, Mod, Max, Min, Date
General Functions
  • Count, Average, Sum, Now etc.
Joining Tables
  • Obtaining data from Multiple Tables
  • Types of Joins (Inner Join, Left Join, Right Join & Full Join)
  • Sub-Queries Vs. Joins
Operators (Data using Group Function)
  • Distinct, Order by, Group by, Equal to etc.
Database Objects (Constraints & Views)
  • Not Null
  • Unique
  • Primary Key
  • Foreign Key
SQL Basic
  • SQL Introduction
  • SQL Syntax
  • SQL Select
  • SQL Distinct
  • SQL Where
  • SQL And & Or
  • SQL Order By
  • SQL Insert
  • SQL Update
  • SQL Delete
SQL Advance
  • SQL Like
  • SQL Wildcards
  • SQL In
  • SQL Between
  • SQL Alias
  • SQL Joins
  • SQL Inner Join
  • SQL Left Join
  • SQL Right Join
  • SQL Full Join
  • SQL Union
SQL Functions
  • SQL Avg()
  • SQL Count()
  • SQL First()
  • SQL Last()
  • SQL Max()
  • SQL Min()
  • SQL Sum()
  • SQL Group By
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Introduction to Data Science

  • What is Analytics & Data Science
  • Common Terms in Analytics
  • What is data
  • Classification of data
  • Relevance in industry and need of the hour
  • Types of problems and business objectives in various industries
  • How leading companies are harnessing the power of analytics
  • Critical success drivers
  • Overview of analytics tools & their popularity
  • Analytics Methodology & problem-solving framework
  • List of steps in Analytics projects
  • Identify the most appropriate solution design for the given problem statement
  • Project plan for Analytics project & key milestones based on effort estimates
  • Build Resource plan for analytics project
  • Why Python for data science

Accessing/Importing and Exporting Data

  • Importing Data from various sources (Csv, txt, excel, access etc)
  • Database Input (Connecting to database)
  • Viewing Data objects - sub setting, methods
  • Exporting Data to various formats
  • Important python modules: Pandas

Data Manipulation: Cleansing - Munging Using Python Modules

  • Cleansing Data with Python
  • Filling missing values using lambda function and concept of Skewness.
  • Data Manipulation steps (Sorting, filtering, duplicates, merging, appending, sub setting, derived variables, sampling, Data type conversions, renaming, formatting.
  • Normalizing data
  • Feature Engineering
  • Feature Selection
  • Feature scaling using Standard Scaler/Min-Max scaler/Robust Scaler.
  • Label encoding/one hot encoding

Data Analysis: Visualization Using Python

  • Introduction exploratory data analysis
  • Descriptive statistics, Frequency Tables and summarization
  • Univariate Analysis (Distribution of data & Graphical Analysis)
  • Bivariate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis)
  • Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc.)
  • Important Packages for Exploratory Analysis (NumPy Arrays, Matplotlib, seaborn, Pandas etc.)

Introduction to Statistics

  • Descriptive Statistics
  • Sample vs Population Statistics
  • Random variables
  • Probability distribution functions
  • Expected value
  • Normal distribution
  • Gaussian distribution
  • Z-score
  • Central limit theorem
  • Spread and Dispersion
  • Inferential Statistics-Sampling
  • Hypothesis testing
  • Z-stats vs T-stats
  • Type 1 & Type 2 error
  • Confidence Interval
  • ANOVA Test
  • Chi Square Test
  • T-test 1-Tail 2-Tail Test
  • Correlation and Co-variance

Introduction to Predictive Modelling

  • Concept of model in analytics and how it is used
  • Common terminology used in Analytics & Modelling process
  • Popular Modelling algorithms
  • Types of Business problems - Mapping of Techniques
  • Different Phases of Predictive Modelling

Data Exploration for Modelling

  • Need for structured exploratory data
  • EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)
  • Identify missing data
  • Identify outliers’ data
  • Imbalanced Data Techniques

Data Pre-Processing & Data Mining

  • Data Preparation
  • Feature Engineering
  • Feature Scaling
  • Datasets
  • Dimensionality Reduction
  • Anomaly Detection
  • Parameter Estimation
  • Data and Knowledge
  • Selected Applications in Data Mining
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Introduction to Machine Learning

  • Artificial Intelligence
  • Machine Learning
  • Machine Learning Algorithms
  • Algorithmic models of Learning
  • Applications of Machine Learning
  • Large Scale Machine Learning
  • Computational Learning theory
  • Reinforcement Learning

Techniques of Machine Learning

  • Supervised Learning
  • Unsupervised Learning
  • Semi-supervised and Reinforcement Learning
  • Bias and variance Trade-off
  • Representation Learning

Regression

  • Regression and its Types
  • Logistic Regression
  • Linear Regression
  • Polynomial Regression

Classification

  • Meaning and Types of Classification
  • Nearest Neighbor Classifiers
  • K-nearest Neighbors
  • Probability and Bayes Theorem
  • Support Vector Machines
  • Naive Bayes
  • Decision Tree Classifier
  • Random Forest Classifier

Unsupervised Learning: Clustering

  • About Clustering
  • Clustering Algorithms
  • K-means Clustering
  • Hierarchical Clustering
  • Distribution Clustering
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Data Science Features

Instructor-led Live SessionsInstructor-led Live Sessions

KVCH experts with in-depth knowledge create a focused learning environment by presenting learners with real-world industry problems and focusing on solutions.

Live Training SessionsLive Training Sessions

During our certified training, seasoned instructors and industry experts conduct remote sessions to share their extensive knowledge with the learners.

Flexible Curriculum Flexible Curriculum

Professionals can obtain in-depth knowledge of cutting-edge digital marketing training by taking advantage of the availability of specialised certificates.

 Expert Support Expert Support

Through a ticketing system that operates around the clock, our technical support staff is available to answer any questions you may have.

Certification Certification

Upon finishing the course and the assigned tasks, you will be awarded a certificate from KVCH, recognising your accomplishment as a data scientist.

Assignments Assignments

There is a quiz at the end of each lesson that must be completed before the next lesson begins to test your understanding.

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Data Science Certification

Earn your certificate

On successful completion of the training, you are awarded with a Certificate in Data Science. The certificate is recognised by top companies and helps in career growth.

KVCH Data Science Certificate holders work at various companies like (TCS, Accenture, Infosys) etc.

Share your achievement

Once you get your certificate, you can share it on your online profiles like LinkedIn. Sharing your certification with your connections will help you acquire your dream job.

Data Science FAQs

What is Data Science ?

Data Science is a subfield of computer science concerned with the use of several algorithms, tools, scientific methodologies, and Machine Learning approaches to extract meaningful information from both organised and unstructured data.

How does one get started in the field of data science ?

The path to becoming a data scientist is open and flexible. Many different technologies and computer languages, including R, Python, and SAS, are used by data scientists. Data analysis, statistical tools, data visualisation, and working with enormous datasets are all skills you should have as an aspiring data scientist. Data analysis and data wrangling take up the vast bulk of a data scientist's work.

What are the different job roles that a data scientist can pursue ?

There are numerous job opportunities available for data scientists, like:

1. Business Analyst
2. Data Analyst
3. Big Data Engineer
4. Data Scientist
5. Statistician
6. Data Engineer
7. Machine Learning Engineer
8. Data Architect

Is this course meant for experienced professionals or beginners in data science ?

It does not matter if you’re a beginner or an experienced professional, anyone interested in data science can enrol in this course.

Does the job assistance program guarantee me a job ?

No, we do not guarantee a job, but we do provide complete placement assistance. The further selection depends on the capability of the student and requirements of the company.

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  • Live, interactive training by experts.
  • Curriculum that focuses on the learner.
  • Challenge-based, hands-on project work.
  • Testing of Expertise in a Variety of Areas.
  • Opportunities for team building.
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  • Completely tailor-made curriculum.
  • Post training support and query management.
  • Regular feedbacks to monitor training effectiveness.
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