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Data Science Course

9,876 Learners 1121 Google Ratings

Ready to dive into the world of data? Our Data Science course is perfect for you! You'll learn how to use Python to crunch numbers and find patterns in big data. We'll teach you how to build models that can predict future trends. With lots of hands-on practice, you'll be ready to solve real problems and start a cool career in data science.

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Data Science Course Overview

The Data Science course offers a comprehensive introduction to the field, equipping learners with essential skills in data analysis, machine learning, and statistical modeling. The course covers key topics such as data wrangling, visualization, and interpretation, using popular tools like Python, R, and SQL. Participants will also explore advanced concepts such as predictive analytics, deep learning, and big data frameworks like Hadoop and Spark. Through hands-on projects and real- world case studies, learners will gain practical experience in applying data science techniques to solve complex business problems, preparing them for roles in industries such as finance, healthcare, and technology.

Additionally, the course focuses on fostering critical thinking and problem-solving skills, ensuring that students can not only manipulate data but also derive meaningful insights from it. Emphasis is placed on real-time data processing, ethical considerations in data use, and emerging trends such as AI integration in data science. By the end of the program, learners will have built a strong foundation in data-driven decision-making, enabling them to contribute to data-centric roles such as data analysts, machine learning engineers, and data scientists. With a combination of theoretical knowledge and practical application, the course prepares participants for a dynamic and growing field.

The course also offers flexibility in learning paths, allowing participants to specialize in areas like natural language processing, computer vision, or business analytics based on their career goals. Industry-standard certifications and capstone projects are included, providing learners with recognized credentials and portfolio-worthy work to showcase their expertise to potential employers. Collaboration with peers and instructors through interactive sessions and workshops ensures a supportive learning environment, fostering both individual growth and team-oriented problem-solving skills. With access to a wealth of resources, including datasets, software, and expert mentorship, students are well-equipped to excel in the ever-evolving world of data science.

Furthermore, the course integrates continuous learning and upskilling opportunities by offering access to an alumni network, career guidance, and ongoing updates in the data science domain. Learners are encouraged to stay ahead of industry trends through participation in hackathons, webinars, and forums where they can interact with professionals and experts in the field. The curriculum is designed to be accessible to both beginners with no prior coding or data experience and professionals seeking to enhance their current skill set. By the end of the program, participants will not only master technical tools but also develop the strategic thinking required to lead data- driven initiatives within organizations, making them invaluable assets in today’s data-centric economy.

Data Science Curriculum

Module 1 - Python for Data Analytics ☰ 6 Topics

Topics: Python Basics

  • Sum / Average / Count / Max / Min
  • Chart
  • Text
  • Need for Programming
  • Advantages of Programming
  • Overview of Python
  • Organizations using Python
  • Python Applications in Various Domains
  • Python Installation
  • Variables
  • Operands and Expressions
  • Conditional Statements
  • Loops
  • Command Line Arguments
  • Different Types of Arguments
  • Global Variables
  • Global Keyword
  • Variable Scope and Returning Values
  • Lambda Functions
  • Various Built-In Functions
  • Introduction to Object-Oriented Concepts
  • Built-In Class Attributes
  • Public, Protected and Private Attributes, and Methods
  • Class Variable and Instance Variable
  • Constructor and Destructor
  • Decorator in Python
  • Core Object-Oriented Principles
  • Inheritance and Its Types
  • Method Resolution Order
  • Overloading
  • Overriding
  • Getter and Setter Methods
  • Inheritance-In-Class Case Study

Topics: Data Structure and File Operations

  • Method of Accepting User Input and eval Function
  • Python - Files Input/Output Functions
  • Lists and Related Operations
  • Tuples and Related Operations
  • Strings and Related Operations
  • Sets and Related Operations
  • Dictionaries and Related Operations

Topics: Functions and Object Oriented Programming

  • User-Defined Functions
  • Concept of Return Statement
  • Concept of name=” main ”
  • Function Parameters

Topics: Working with Modules and Handling Exceptions

  • Standard Libraries
  • Packages and Import Statements
  • Reload Function
  • Important Modules in Python
  • Sys Module
  • Os Module
  • Math Module
  • Date-Time Module
  • Random Module
  • JSON Module
  • Regular Expression
  • Exception Handling

Topics: Introduction to NumPy

  • Basics of Data Analysis
  • NumPy - Arrays
  • Operations on Arrays
  • Indexing Slicing and Iterating
  • NumPy ArrayAttributes
  • Matrix Product
  • NumPy Functions
  • Functions
  • Array Manipulation
  • File Handling Using NumPy
  • Array Creation and Logic Functions
  • File Handling Using Numpy

Topics: Data Manipulation using pandas

  • Introduction to pandas
  • Data structures in pandas
  • Series Data Frames
  • Importing and Exporting Files in Python
  • Basic Functionalities of a Data Object
  • Merging of Data Objects
  • Concatenation of Data Objects
  • Types of Joins on Data Objects
  • Data Cleaning using pandas
  • Exploring Datasets
Module 2 - Data Science Primer and Statistics ☰ 4 Topics

Topics: Basics of Data Science

  • What is Data Science?
  • What does Data Science involve?
  • Era of Data Science
  • Business Intelligence vs Data Science
  • Life cycle of Data Science
  • Tools of Data Science
  • Application of Data Science

Topics: Exploratory Data Analysis

  • Introduction
  • Stages of Analytics
  • CRISP DM Data Life Cycle
  • Data Types
  • Introduction to EDA
  • First Business Moment Decision
  • Second Business Moment Decision
  • Third Business Moment Decision
  • Fourth Business Moment Decision
  • Correlation

Topics: Feature Engineering

  • What is Feature
  • Feature Engineering
  • Feature Engineering Process
  • Benefit
  • Feature Engineering Techniques

Topics: Inferential Statistics & Hypothesis Testing

  • Basics Of Probability
  • Discrete Probability Distributions
  • Continuous Probability Distributions
  • Central Limit Theorem
  • Concepts Of Hypothesis Testing - I: Null And Alternate Hypothesis, Making A Decision, And Critical
  • Value Method
  • Concepts Of Hypothesis Testing - II: P-Value Method And Types Of Errors Industry Demonstration
  • Of Hypothesis Testing: Two-Sample Mean And Proportion Test, A/B Testing
Module 3 - Machine Learning ☰ 10 Topics

Topics: Linear Regression

  • Simple Linear Regression
  • Simple Linear Regression In Python
  • Multiple Linear Regression
  • Multiple Linear Regression In Python
  • Industry Relevance Of Linear Regression

Topics: Logistic Regression

  • Univariate Logistic Regression
  • Multivariate Logistic Regression: Model
  • Building And Evaluation
  • Logistic Regression:
  • Industry Applications

Topics: KNN classifier

  • Data mining classifier technique
  • Application of KNN classifier
  • Lazy learner classifier
  • Altering hyperparameter(k) for better accuracy

Topics: Support Vector classifier

  • Black box
  • SVM hyperplane
  • Max margin hyperplane
  • Kernel tricks for non linear spaces

Topics: Decision Tree Classifier

  • Rule based classification method
  • Different nodes for develop decision trees
  • Discretization
  • Entropy
  • Greedy approach
  • Information gain

Topics: Ensemble Learning

  • Challenges with standalone model
  • Reliability and performance of a standalone model
  • Homogeneous & Heterogeneous Ensemble Technique
  • Bagging Boosting
  • Random forest
  • Stacking
  • Voting & Averaging technique

Topics: Time Series Analysis

  • Difference between cross sectional and time series data
  • Different component of time series data
  • Visualization techniques for time series data
  • Model based approach
  • Data driven based approach

Topics: Clustering

  • Difference between Supervised and Unsupervised Learning
  • Prelims of clustering
  • Measuring distance between record and groups
  • Linkage functions
  • Dendrogram

Topics: Dimensionality Reduction

  • Dimension reduction
  • Application of PCA
  • PCA & its working
  • SVD & its working

Topics: Market Basket Analysis

  • Point of Sale
  • Application of Association rules
  • Measure of association rules
  • Drawback of measure of association rules
  • Condition probability
  • Lift ratio
Module 4 - Deep Learning ☰ 3 Topics

Topics: Introduction to Perceptron, Multilayer Perceptron/ANN

  • Black box techniques
  • Intution of neural networks
  • Perceptron algorithm
  • Calculation of new weights
  • Non linear boundaries in MLP
  • Integration function
  • Activation function
  • Error surface
  • Gradient descent algo

Topics: Deep Learning Black Box Technique - CNN, RNN

  • Imagenet classification challenges
  • Convolution network applications
  • Challenges in classifying the images using MLP
  • Parameter explosion
  • Pooling layers
  • Fully connected layers
  • Alexnet case study
  • Modelling sequence data
  • Vanishing/Gradient descent explode

Topics: Platforms for Deep Learning & Deep Learning Software Libraries

  • What is a Deep Learning Platform?
  • H2O.ai
  • Dato GraphLab
  • What is a Deep Learning Library?
  • Theano
  • Deeplearning4j
  • Torch
  • Caffe
Module 5 - Data Visualization and Story Telling ☰ 3 Topics

Topics: Basic Visualization Tools

  • Bar Charts
  • Histograms
  • Pie Charts
  • Box Plots

Topics: Basic Visualization Tools Continued

  • Scatter Plots
  • Line Plots and Regression

Topics: Specialized Visualization Tools

  • Pair plot
  • Word Clouds
  • Radar Charts
  • Waffle Charts
Module 6 - Natural Language Processing ☰ 1 Topics

Topics: Text Mining & Natural Language Processing

  • Text data generating sources
  • How to give structure to text structure using bag of words
  • Terminology used in text data analysis
  • DTM & TDM
  • TFIDF & its usage
  • Word cloud and its interpretation
Module 7 - SQL ☰ 5 Topics

Topics: Getting Started and Creating, Selecting & Retrieving Data with SQL

  • Introduction to Databases
  • How to create a Database instance on Cloud?
  • Provision a Cloud hosted Database instance.
  • What is SQL?
  • Thinking About Your Data
  • Relational vs. Transactional Models ER Diagram
  • CREATE Table Statement and DROP tables
  • UPDATE and DELETE Statements
  • Retrieving Data with a SELECT Statement
  • Creating Temporary Tables
  • Adding Comments to SQL

Topics: Filtering, Sorting, and Calculating Data with SQL

  • Basics of Filtering with SQL
  • Advanced Filtering: IN, OR, and NOT
  • Using Wildcards in
  • SQL Sorting with ORDER BY
  • Math Operations
  • Aggregate Functions
  • Grouping Data with SQL

Topics: Subqueries and Joins in SQL

  • Using Subqueries
  • Subquery Best Practices and Considerations
  • Joining Tables
  • Cartesian (Cross) Joins
  • Inner Joins
  • Aliases and Self Joins
  • Advanced Joins: Left, Right, and Full Outer Joins
  • Unions

Topics: Modifying and Analyzing Data with SQL

  • Working with Text Strings
  • Working with Date and Time Strings
  • Date and Time Strings Examples
  • Case Statements
  • Views
  • Data Governance and Profiling
  • Using SQL for Data Science

Topics: Accessing Databases using Python

  • How to access databases using Python?
  • Writing code using DB-API
  • Connecting to a database using DB API
  • Create Database Credentials
  • Connecting to a database instance
  • Creating tables, loading, inserting, data and querying data
  • Analyzing data with Python
Module 8 - Excel ☰ 1 Topics

Topics: Text Mining & Natural Language Processing

  • Input data & handling large spreadsheets
  • Tricks to get your work done faster
  • Automating data analysis (Excel VLOOKUP, IF Function, ROUND and more)
  • Transforming messy data into shape
  • Cleaning, Processing and Organizing large data
  • Spreadsheet design principles
  • Drop-down lists in Excel and adding data validation to the cells.
  • Creating Charts & Interactive reports with Excel Pivot Tables, Pivot
  • Charts, Slicers and Timelines
  • Functions like: - COUNTIFS, COUNT, SUMIFS, AVERAGE and many more.
  • Excel features: Sort, Filter, Search & Replace Go to Special etc...
  • Importing and Transforming data (with Power Query)
  • Customize the Microsoft Excel interface
  • Formatting correctly for professional reports.
  • Commenting on cells.
  • Automate data entry with Autofill and Flash-fill.
  • Writing Excel formulas & referencing to other workbooks/worksheets.
  • Printing options
  • Charts beyond column and bar
  • Charts: - Pareto chart, Histogram, Tree map, Sunburst charts & more
Module 9 TABLEAU ☰ 1 Topics

Topics: Analyzing and Visualizing Data using Tableau

  • Introduction to Data Visualization
  • Tableau Introduction and Tableau Architecture
  • Exploring Data using Tableau
  • Working with Data using Tableau including Data Extraction and Blending
  • Various Charts in Tableau (Basics to Advanced)
  • Sorting-Quick Sort, Sort from Axis, Legends, Axis, Sort by Fields
  • Filtering- Dimension Filters, Measure Filters, Date Filters, Tableau Context Filters Groups, Sets and
  • Combined Sets
  • Reference Lines, Bands and Distribution
  • Reference Lines, Bands and Distribution
  • Parameters, Dynamic Parameters and Actions
  • Forecasting-Exponential Smoothening Techniques
  • Clustering
  • Calculated Fields in Tableau, Quick Tables
  • Tableau Mapping Features
  • Tableau Dashboards, Dashboards Action and Stories
Module 10 - POWER BI ☰ 5 Topics

Topics: Introduction To Power BI

  • Introduction to Power BI-Need, Imprtance
  • Power BI - Advantages and Scalable Options
  • Power BI Data Source Library and DW Files
  • Business Analyst Tools, MS Cloud Tools
  • Power BI Installation
  • Power BI Desktop - Instalation, Usage
  • Sample Reports and Visualization Controls
  • Understanding Desktop & Mobile Editions
  • Report Rendering Options and End User Access

Topics: Creating POWER BI Reports, Auto Filters

  • Report Design with Databse Tables
  • Report Visuals, Fields and UI Options
  • Reports with Multiple Pages and Advantages
  • Pages with Multiple Visualizations. Data Access
  • “GET DATA” Options and Report Fields, Filters
  • Report View Options: Full, Fit Page, Width Scale
  • Report Design using Databases & Queries

Topics: Report Visualization And Properties

  • Power BI Design: Canvas, Visualizations, and Fileds
  • Import Data Options with Power BI Model, Advantages
  • Data Fields and Filters with Visualizations
  • Visualization Filters, Page Filters, Report Filters
  • Creating Customised Tables with Power BI Editor
  • General Properties, Sizing, Dimensions, and Positions
  • Grid Properties (Vertical, Horizontal) and Styles
  • Page Level Filters and Report Level Filters
  • Visual-Level Filters and Format Options
  • Report Fields, Formats and Analytics

Topics: Chart And Map Report Properties

  • CHART Report Types and Properties
  • STACKED BAR CHART, STACKED COLUMN CHART
  • CLUSTERED BAR CHART, CLUSTERED COLUMN CHART
  • Field Properties
  • Formats: Legend, Axis, Data Labels, Plot Area
  • Data Labels: Visibility, Color and Display Units
  • Analytics: Constant Line, Position, Labels
  • Map Reports: Working with Map Reports
  • Hierarchies: Grouping Multiple Report Fields
  • Advanced Query Mode @ Connection Settings - Options

Topics: DAX EXPRESSIONS

  • Purpose of Data Analysis Expresssions (DAX)
  • Scope of Usage with DAX. Usabilty Options
  • DAX Context: Row Context and Filter Context
  • DAX Entities: Calculated Columns and Measures
  • DAX Data Types: Numeric, Boolean, Variant, Currency
  • Datetime Data Tye with DAX. Comparison with Excel
  • DAX Operators & Symbols. Usage. Operator Priority
  • Parenthesis, Comparison, Arthmetic, Text, Logic
  • DAX Functions and Types: Table Valued Functions
  • Filter, Aggregation and Time Intelligence Functions
  • Information Functions, Logical, Parent-Child Functions
  • Statistical and Text Functions. Formulas and Queries
  • Syntax Requirements with DAX. Differences with Excel
  • Naming Conventions and DAX Format Representation
  • Working with Special Characters in Table Names
  • Attribute / Column Scope with DAX-Examples
  • Measure / Column Scope with DAX-Examples

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Data Science Tools and technology Covered

  • anaconda courseAnaconda
  • tableau courseTableau
  • Power-BI-SymbolPower-Bi
  • imageggplotggplot
  • imagejupitorJupyter
  • imagenumpyNumpy
  • imagepythonPython
  • imagepandasPandas
  • imageseabornSeaborn
  • imagelockerLooker
  • matplotlib courseMatplotlib
  • imagepycharmPyCharm
  • imagegoogleColabGoogle Colab
  • imagenltkNLTK
  • imagescikitlernScikit-learn
  • imagesqlSQL
  • imageMySqlMySql
  • imagepostgersqlPostgreSQL
  • imagemlML
  • imagedeeplDeep Learning
  • imagenlpNLP

Why Choose Data Science Course from KVCH

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Instructor-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.

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Live Training Sessions

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

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Flexible Curriculum

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

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Expert Support

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

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Certification

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

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Assignments

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

Sample Certificate

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Data Science Course Certification

Earn your certificate

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

KVCH Data Science Course 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.

Testimonials & Reviews

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Deepak Mandal (Python Certification Training)
Tuesday Dec 5, 2023

KVCH's Python Certification Training courses have been a game-changer for me. The learning experience is top-notch, offering a good balance of theory and hands-on practice. The instructors are fantastic, explaining complex concepts clearly and engagingly. The course structure is well-designed, making it easy for students to grasp Python programming.

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JAY PRAKASH THAKUR (Software Testing Training)
Thursday, Sep 7, 2023

So, I finished the software testing thing at KVCH, and man, I am really really happy ya know? Like, seriously. The teachers there, they aree like pros or something. They teach you all this testing stuff, and it is not boring theory, it is like real things you do in a job.And they are patient too, even with my silly questions. The people who work there are nice too, they helped me find my way when I got lost on the first day. Now, I'm feeling like I could actually do this testing job. Like, for real. The stuff I learned is already helping me when I talk about things in interviews. So, yeah, if you wanna do software testing and not be bored to death, go to KVCH. Super recommended!

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Sachin Pal (Python Certification Training)
Thursday Nov 16, 2023

My experience with KVCH was quite well and I have learned a lot .. Equal attention was given to every individual which is the best part of KVCH and faculties.. So I would suggest if you're really interested in law you should try KVCH.. It helps u overcome your fears and helps you, In achieving your goals..

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Anurag kumar (Software Testing Training)
Tuesday Dec 5, 2023

KVCH's Python Certification Training courses have been a game-changer for me. The learning experience is top-notch, offering a good balance of theory and hands-on practice. The instructors are fantastic, explaining complex concepts clearly and engagingly. The course structure is well-designed, making it easy for students to grasp Python programming.

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Sandeep Kashyap (Machine Learning Training)
Friday, July 7, 2023

I did the machine learning course for two months. It was very helpful and the hours are flexible so it is great place for anyone who is looking to learn new skills even if you have a busy schedule.

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Shruti Mahendru (Digital Marketing Training)
Friday, Sep 8, 2023

I have enrolled for digital marketing master course and guys seriously I love the course, trainer is very experienced and i got paid internship after course completion. Must recommend best digital marketing institute in Noida.

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Shivam Chauhan (Java Training)
Monday, Aug 7, 2023

My 6-week Java internship at KVCH Noida was truly exceptional. The institute's commitment to providing hands-on learning and real-world projects allowed me to dive deep into Java programming. The mentors were incredibly supportive, always ready to guide and share their expertise. I am grateful for the valuable skills and knowledge I gained during my time here. Thank you, KVCH Noida, for a rewarding and transformative experience! 🙏📚"

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vishesh shukla (Java Training)
Friday, April 7, 2023

I have pursued the course of Complete Java from the institute. The Teaching staff and the management staff is such a great person. They help with every problems during the course and even after the completion of the course. The trainer is such a knowledge full skilled working industrialist having a great knowledge. Aalways helpful and great trainer too.

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Saumya Trivedi (6 Months Java Training)
Wednesday, Dec 7, 2022

I have pursued the course of Complete Java from the institute. The Teaching staff and the management staff is such a great person. They help with every problems during the course and even after the completion of the course. The trainer is such a knowledge full skilled working industrialist having a great knowledge. Aalways helpful and great trainer too.

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Amar Sagar Rawat (Digital Marketing Training)
Wednesday, Dec 7, 2022

I did my digital marketing training under the guidance of Mr.Ajay Sharma Sir. Thanks to him, he has in-depth knowledge and he has good experience in this field. I will recommend his classes. My overall experience till now has been very good with KVCH Noida.

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Sweta Padma Prusty (Web Development Training)
Friday, Dec 9, 2022

Just completed my 6 weeks training in Web Development using Python under the guidance of Saurabh Sir. The course was well structured and helped me build better concepts. My counselor - Aarti Ma'am was also very helpful right from the beginning till the end. Had a great time learning.

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Kissu Malakar (Android Training)
Friday, Dec 7, 2018

It was really a good experience with kvch. I have done the Android training from here and our instructor ( Ginni mam) was very helpful and friendly when ever we needed help regarding any query she was there to help us.

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Sumit Vaishanv (Digital Marketing Training)
Wednesday, Dec 7, 2022

Thank you so much KVCH today I have done my complete digital marketing training. I am so happy because you have best trainer of digital marketing & really your trainer have brilliant to industrial industry expert and thank you so much my trainer Ajay sir to provide me best training. now I would like say everyone which want to like make career in digital marketing then you should also join KVCH Because KVCH Training company is the best option for you. He provides best training with 100% placement. now thanks again KVCH for Digital marketing training.

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Himanshu Sharma (Data Science Training)
Wednesday, Dec 8, 2021

I just completed my Data Science training course from KVCH. It was a money well-spent experience. My trainer was Mr. Rohan. He was a very patient and skilled mentor. Always taught the topics in simple terms and made sure everyone understood. The course content provided to the students was very helpful. Thank you KVCH for this.

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sharmila sheoran (6 Weeks Industrial Training)
Saturday, Oct 8, 2022

Thank you kvch for providing me great learning, I have completed my 6 weeks industrial training under the guidance of saurabh sir. The course was well structured and help me to build better concepts. My teacher and counselor - Saurabh Srivastava Sir and Aarti mam both were very helpful and kind.

frequently asked questions

What are the basics required to learn data science?

SQL, R, and Python for programming. Statistical procedures in statistical analysis. TensorFlow, PyTorch, Scikit-Learn, and algorithms are used in machine learning. Tableau, Power BI, and Matplotlib are tools for data visualization.

What is the biggest challenge in data science?

The biggest challenge in data science is often data quality and quantity. Data can be noisy, inconsistent, or incomplete, and large datasets can be difficult to manage.

What is the future of data science course?

Data Science has a bright future with an expected growth rate of 22% by 2030. This field is constantly evolving and uses various techniques to extract knowledge from data. Data scientists are in high demand due to the increasing amount of data companies collect and their inability to handle it effectively.

Can I become a data scientist in 3 Months?

Becoming a proficient data scientist in three months is challenging. It requires a solid foundation in statistics, programming, and machine learning, along with practical experience and continuous learning. While it's possible to start this journey in a short timeframe, building expertise takes time and dedication.

Who is eligible for data science course?

Anyone with a strong background in math, statistics, or computer science can benefit from a data science course. If you have these skills and a passion for analyzing data, you're a good candidate.

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