Data Science Course with Online Internship.

Welcome to the best data science course and data science internship, seamlessly merged into a comprehensive online data science program.

Our data science course is meticulously crafted to equip you for the roles of Data Scientist, Data Analyst, Business Analyst, or ML Engineer.

In just 60 days, you’ll master the tools and techniques wielded by Data Scientists, enabling you to conquer challenges and execute critical tasks essential for an organization’s success.

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About: Data Science Training & Internship Program.

CF DORI offers a dynamic hybrid data science course that seamlessly integrates training and internships.

Our program is designed for individuals at all skill levels, including beginners. You’ll embark on a practical journey, applying your newfound knowledge to hands-on projects. Whether you aspire to become a Data Scientist or are considering a career change, this internship is an ideal starting point, and prior relevant experience is not necessary.

In just 60 days, our comprehensive program equips you with the skills needed to thrive as a Data Scientist. You’ll master data collection, storage, visualization, processing, and statistical analysis using essential tools such as Python, R, SQL, MS Excel, and data visualization libraries like Matplotlib and Seaborn. Join us for a transformative learning experience in the world of data science.

Course with Internship?

In our data science training and internship program, theory and practice go hand in hand. You’ll begin by mastering key concepts, followed by hands-on projects that reinforce your learning.

In just 60 days, you’ll gain valuable data science skills through real industry projects. These projects can bolster your resume and LinkedIn profile, serve as college project examples, or give you an edge in future job or internship opportunities.

As a bonus, upon completion, you’ll receive two certificates: a 2-month “Certificate of Training” and a 2-month “Certificate of Internship,” highlighting your Data Science Course and Internship experience.

What Will You Learn:

On the first day you will get complete information about the Curriculum & Classes.

    • What is Data Science? What does a data scientists do?
    • The need for data science / Problems solved with Data Science.
    • Big Data
    • Difference between AI, ML and DL
    • How tech giants use data (for customer preferences).
    • How does Netflix and Amazon use data to build their recommendation model.
    • The Companies using Python. Popular Applications where Python is used.
    • Python Environment Setup (Jupyter).
    • Fundamentals: Variables, Numbers and Boolean, Strings, Arithmetic Operators, The Double Equality Sign, Reassign Values, Add Comments, Line Continuation, Indexing Elements, Structure Your Code with Indentation, Comparison Operators, Logical and Identity Operators.
    • Data Types: Immutable – [Numbers, Strings, Tuples]; Mutable – [Lists, Dictionaries, Sets] and related operations.
    • Loops: For Loops (break, continue, pass) & While Loops
    • Functions: creating and calling functions, lambda, filter, map, in-built functions, user defined functions
    • Condition: if, if-else, nested if-else, else-if
    • Library: What is library, what is package, how to create packages, Introduction to PIP, Namespace, Using Python Packages, Installing Packages via PIP
    • An intro to NumPy : Few related operations
    • Mathematical computing with Python (NumPy)
    • Array: Data Types in an Array, Dimensions of an Array, Operations on Array: Indexing, Slicing, Splicing, Sub-setting
    • PandasPandas Setup & Intro, Loading Data into Pandas, Reading Data, Sorting Values, Adding or Removing Columns, Rearranging Columns, Saving Data, Filtering Data, Filtering Based on Textual Patterns, GroupBy, Chunksize, DataFrame, Panel, Reindexing, Iteration, Sorting, Working with Text Data, Merging/Joining, Concatenation, Date Functionality, Categorical Data
    • Introduction: Introduction of R, R Installation, Basic Structure of R program with a simple program, Constants, variables and declarations, Simple Input-Output statements, Operators – Arithmetic, Relational and Logical

    • Data Types in R: What are datatypes in R?, Types of data types in R, Numeric Datatypes, Integer Datatypes, Logical Datatypes, Complex Datatypes, Character Datatypes, Vectors Datatypes, List Datatypes, Matrices Datatypes, Arrays Datatypes, Factors Datatypes, Data Frames in R

    • Variables in R: Variables Definition, Declaring and Initialising variables, Syntax of declaring variables, Important method for variables, Scope of variables, Dynamic scoping in R Variables, Lexical scoping in R variables, Data type of a variable, Finding variables, Deleting variables

    • Control Flow: Control statement in R program, What are control statements, If conditions, If-else conditions, For Loop, Nested Loop, While loop, Repeat loop and break statements, Return statements, Next statement, Decision making in R prog with flow chart, Switch case in R, For loop in R, While loop in R, Repeat loop in R, Go to statements in R, Break and next statements in R

    • Functions: Function Definition, Function components, Built-In functions, User defined functions, Single input- single output, Multiple Input – Multiple Output, Inline function, Passing arguments of a function, Lazy evaluation of a function, Function argument in R programming, Adding an argument in R, Adding multiple arguments in R, Adding default arguments in r, Dots arguments, Function as an argument, Type of function in R programming, How to define a function?, Calling a function, Types of function, Primitive function, Infix function, Replacement function, Recursive function, Application of recursive function in R programming, Conversion of function.

    • Data Structures: What are data structures in R programming?, R-Strings, R-Vectors, R-List, R-arrays, R-Matrices, R-Factors, R-Data frames

    • R- Object Oriented Programming: Classes and Objects, Creating S3 class, Generic function, Attributes, Classes in R programming, R-Objects, Encapsulation in R programming, Polymorphism in R programming, R-Inheritance, Abstraction in R programming

    • Error Handling: Handling error in R Programming, Condition handling in R program, Debugging in R program

    • File Handling: File handling in R Programming, Reading files in R Programming, Writing files in R Programming, Binary files in R Programming

    • Data Interfaces: Data Handling in R Programming, Importing data in R Programming, Exporting data in R Programming, Working with CSV files in R Programming, Working with XML files in R Programming, Working with EXCEL files in R Programming, Working with JSON files in R Programming, Working with DATABASES in R Programming
    • R-Statistics
    • Mean, median and mode in R programming
    • Calculate the average, variance and standard deviation in R Programming
    • Descriptive analysis in R Programming
    • Normal distribution in R Programming
    • Binomial distribution in R Programming
    • ANOVA test in R Programming
    • Co-Variance and Correlation in R Programming
    • SKEWNESS and KURTOSIS in R Programming
    • Hypothesis testing in R Programming
    • Bootstrapping in R Programming
    • Time series analysis in R Programming
    • Facebook – Chatbot Army
    • Twitter – Curated Timelines
    • Google – Neural Networks and ‘Machines That Dream’
    • Edgecase – Improving Ecommerce Conversion Rates
    • Baidu – The Future of Voice Search
    • HubSpot – Smarter Sales
    • What is Machine Learning?
    • Phases, Advantages, Applications and Types of Machine Learning
    • Supervised Learning in Depth
    • Introduction to Different Algorithms of Regression & Classification
    • Evaluation Metrics of Regression & Classification
    • Model Flow in Machine Learning
    • Creating a Machine Learning Model
    • Different types of data
    • Types of Statistical Analysis
    • Z Test, T Test, Chi-Square Test
    • Understanding Decision Tree
    • Understanding Ensemble Models – Bagging and Boosting
    • Clustering Algorithms – K means clustering
    • Introduction to Deep Learning
    • A project on Artificial Neural Network
    • A Glimpse of CNN and RNN
    • Introduction to NLP
    • Complete Project on Machine Learning
    • Classification Model Building
    • Confusion Matrix
    • Precision, Recall, F1 Score, Accuracy, ROC Curve, AUC Curve and Statistics

  1. Data Visualization with R
    • Data Visualization Meaning
    • R-Line Graphs
    • R-Bar graphs
    • Histogram
    • Scatter plots
    • R-Pie charts
    • Boxplots

  2. Data Visualization with Python
    • Matplotlib: Data Visualization on Matplotlib, Bar Plot, Histogram Plot, Box Plot, Area Plot, Scatter Plot, Pie Plot
    • Seaborn: Introduction to Seaborn, Matplotlib vs Seaborn, Distribution Plot, Joint Plot, Hexagon Distribution, KDE Plot, Pair Plot, Rug Plot, Styling, Bar Plot, Count Plot, Box Plot, Violin Plot, Strip Plot, Swarm Plot, Palettes, Heatmaps, Cluster Map, Pair Grid, Facet Grid, Regression Plots
    • What are Data Operations?
    • Tour of Excel
    • Excel Worksheets
    • Excel Ribbon
    • Quick Access Toolbar
    • Keyboard Shortcuts
    • Rows & Columns
    • Transpose
    • Find and Replace (with * and Newline usage)
    • Formulas
    • Excel Functions
    • Reorder and Summarize Data
    • Sorting & Filtering
    • Pivot Tables & Charts
    • Combine Data from Multiple Sources
    • Sheet Protection
    • Print Worksheets
    • Why SQL for Data Science ?
    • Getting Started with SQL.
    • Data types in SQL
    • Basic SQL Queries + Joins
    • SQL Constraints : Concept of Keys
    • Introduction to Relational DB and Tables.
    • SQL Functions

After the training you will go through a placements preparation consisting of the following modules.

1. LinkedIn Profile Building
2. Resume Building
3. GD Tips
4. Personal Interview Prep (P.I.)
5. and more.

Here is the list of projects we are doing in this CF-DORI Training + Internship.


  1. Programming – Student Portfolio
  2. Programming – Rock Paper Scissor
  3. Programming – Multiplication Tables Generator
  4. Programming – Real Calculator
  5. Programming – Bulk File Rename
  6. Programming – News Application with Tkinter
  7. NumPy & Pandas – Handling Real World Financial Data with Pandas
  8. Machine Learning – Music Preference Predictor
  9. Machine Learning – Car Price Prediction using Regression Model
  10. Machine Learning – Credit Card Defaulters prediction using ANN
  11. Data Visualization – Covid Data Analysis with Matplotlib
  12. SQL – Five Real Life Corporate Assignments
  13. Python & SQL – Own Database Management System
  14. MS Excel – Colleges Data Cleaning & Entry using MS Excel

Any topic of any module can be modified before the commencement of the training. Please check the final curriculum prior to starting the course.

Learning Timeline :

Placement Assistance?

Complete our comprehensive Data Science course, and you’re not just equipped with knowledge – you’re prepared for success. Our Placement Assistance Program goes the extra mile to ensure your career takes off.

We directly connect you with HR teams at top companies, vouching for your skills and opening doors to roles like Data Engineer, Analyst, Scientist, and more. Salaries range from 4 to 12 lakhs per annum, with an average of 6 lakhs.

We empower you with industry-standard skills and guide you through hands-on projects, designed by experts. You’ll walk into interviews confidently, armed with 14 projects and proficiency in 12 essential tools.

Excel and you’ll earn our prestigious “Certificate of Distinction” to showcase your achievements on your resume. Join us for a career journey that’s backed by expertise and opportunity.

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Available Certificates :

After the completion of 14 data science internship projects. Interns will get a “Certificate of Internship” which will state about your duration of internship with us. To get this certificate you must complete at-least 8 hands-on projects out of 14.
After the completion of all the modules of the data science course (training). Students will be eligible to get a “Certificate of Completion”.  The quiz scores should be above 45%.
A “Certificate of Distinction” is based on various different factors of a student. It is provided to the top performing students who has more than 90% average quiz marks and has completed and submitted all the projects; and is found actively helping other interns.
College Finder Internship Certificate 1
Training Certificate 1
College Finder Distinction Certificate 1

Learn Data Science with

Python Programming Machine Learning NumPy, Pandas SQL Database R Programming Microsoft Excel Data Visualization Advanced Statistics Matplotlib, Seaborn and become a Data Scientist.

Data Science Training 100% Money back

Admissions Open
₹ 5,799 ₹ 1799

Offer Valid for October 2, 2023

Contact Us

If you have any questions concerning the internship, please reach out to us.

Frequently Asked Queries:

A data scientist’s role combines coding and statistics. Their job is to analyze, process, and model data/information to interpret the results and create actionable plans for organizations based on the outcome.

Yes, coding knowledge is mandatory to become a proficient data scientist. Daily tasks as a data scientist involve using Python and R languages to clean, manipulate, and extract insights from data.

There are no boundaries to learning data science. We welcome candidates from any field of study, regardless of age.

If you have an interest in learning Data Science and can understand English, you’re eligible.

Absolutely, our training begins with the absolute basics and progresses to advanced levels.

The program is designed for beginners and moderate-level students, including college students and working professionals new to the field of Data Science.

We use Google Colab for high-computing tasks, so strict system requirements are not needed.

A basic laptop with a good internet connection will suffice. There are no prerequisites as we start the course from the basics.

There is no last date; registrations are open continuously.

However, you may miss out on any current offers available.

We provide recorded classes to accommodate students with varying schedules and time zones.

You have the flexibility to choose your study time and access the course content, projects, and schedules as per your availability.

For Academic Support :

For academic support, you can pause the video during lessons to ask questions in the Q&A panel, where mentors will respond. Additionally, there’s a LinkedIn group for student interaction and doubt discussions.

For Technical/General Support :

For technical or general support, you’ll be assigned a dedicated counselor who can assist you via WhatsApp.

 Simply click here to access the registration form. Fill it out and proceed with the checkout process.

Plan to allocate around 60 minutes a day for approximately 60 days.

The timing is flexible, allowing you to study at your convenience. About 60 minutes of content unlocks daily.

Absolutely! The course is designed to be flexible and is suitable for individuals from all fields of study.

We start from the ground up, making it accessible to complete beginners.

Yes, you can obtain the course curriculum in PDF format by clicking on the Syllabus PDF button given below this FAQ section.

Yes, we offer a part-payment option. You can pay 500/- to reserve your seat and settle the rest one week before the batch start date.

For more details, contact your counselor or use the live chat feature.

This program is entirely online, and there are no physical classroom sessions or offline interactions required.

Yes, you can take a 120 Days free extension anytime for future reference of course material after completing the program, allowing you to revisit and review the content as needed.

Yes, our course materials are responsive and accessible on various devices, including mobile phones, making it convenient for you to learn on the go.

Our training program is budget-friendly because we prioritize affordability over hefty advertising costs.

By sharing our course with your network, you help us expand across cities and provide access to quality education for all.

Data Science Course - Online Training and Internship
rsz new no price

This is a data science program that is a fusion of two programs, a data science course for beginners and a data science entry-level internship. Get trained by 12 international trainers from Malaysia, Kenya, Yemen, and India, and get hands-on experience with 14 different projects throughout the journey to get prepared for the role of Data Scientist in the future.In only 60 days, you will be confident with using the tools used by Data Scientists to tackle challenges and complete various life-saving tasks for the organization’s survival!

Course Provider: Organization

Course Provider Name: College Finder

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