Data Science Course for Beginners – Online Training and Internship

Data Science Course with Online Internship.

Here’s an online data science course and data science internship, fused together to make the perfect data science program.

This data science course is designed with the purpose to prepare candidates for the role of Data Scientist, Data Analyst, Business Analyst or ML Engineer.

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!

You will be alloted the next nearest batch to your selected date.
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About: Data Science Training & Internship Program.

CF DORI is a supercharged hybrid data science course which provides training and internship together.

Here you will learn Data Science from a beginner level and work on hands-on projects based on what you’ve learned. This internship is perfect if you’re interested in becoming a Data Scientist or having an interest in starting or restarting your career in this field. Previous relevant knowledge is not required.This program will give you the skills to be a fresh Data Scientist in 60 days. You’ll learn how to collect, store, visualize, process, and use statistics to get valuable insights from data using various tools like Python, R, SQL, and MS Excel as well as data visualization tools like Matplotlib and Seaborn.

Course with Internship?

Overall the data science training and the data science internship go parallel to each other. You first learn the topic and then apply the concepts on the hands-on project before moving to the next topic.

In only, 60 days you will acquire data scientific skills while working on industry projects which you can further add to your resume and LinkedIn profile. You can also use the projects to show as your college project, or to get advantage in future job or internships.
Additionally, you also get a 2-month “Certificate of Training” and a 2-month “Certificate of Internship” for the Data Science Course and Internship experience.

What You'll 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 :

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 March 31, 2023

Placement Assistance?

At the end of this course, there’s a Placement Preparation Program in which we prepare you for placements. Besides, we directly share your resume with the HR of current vacant companies, recommending your placement. You can expect roles such as Data Engineer, Data Analyst, Business Analyst, Data Scientist or Data Operations.The placements will range from 4lpa to 12lpa. 6lpa being average.

Overall, we help you reach interview rooms. However, post that your selection depends on your caliber. We train you with required skills and provide you guidance in working on industry-standard data science projects. The curriculum is designed by experts working in the data science domain, we firmly believe that after completing the program you can walk into interviews and get an advantage over the other applicants with you 14 hands-on projects and the 12 tools mentioned in resume.

Moreover, a “Certificate of Distinction” is provided to the best performers based on various assessment parameters which can be showcased on your resume as an achievement. 

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.

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 analyse, 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 good data scientist. The day-to-day work as a data scientist will require the utilization of Python and R languages. To clean, or manipulate the data and extract information out of it.

To learn data science there are no boundaries. We allow candidates from any field of study without age criteria.

You can join if you are interested in learning Data Science; whether to shift to the IT domain or to start a career in Data Science.

Candidates should be able to understand English.

Yes, we start from absolute basics and take you to the advanced level. The training is for beginners and moderate-level students.

Most of the candidates are college students or working professionals, who are new in the field of Data Science.

We will use Google Colab when high computing power is needed, hence there are no strict system requirements. You need good internet connectivity on any basic laptop.

Also, there are no pre-requisites as we are starting the data science course topics from basics.

There is no last date, the registrations will be ongoing all the time.

You may miss any offers currently available.

We Provide Recorded Classes.

We have students from different backgrounds some have day or night-shift jobs some have colleges so all can’t sit at the same time for sessions.

What we do, is we record the lessons and stream them according to the country’s time (being in different timezones also is an issue, as we allow international applications).

You get the sessions, files, projects, and your daily schedule, and you are free to select any time slot of the day, based on your availability, and complete the tasks.

Also to cover the huge syllabus in a short time, we have to edit out the unnecessary parts like breaks to reduce class duration, typing, etc. and add transitions for better visibility.

For Academic Support :

Just like in live classes, while watching the video, as soon as you get a doubt you can pause the video and ask your query there itself, in the QnA panel where mentors will reply to their doubts.


Moreover, in the first lesson, we have shared the link to a LinkedIn group, where students can interact with each other and also discuss doubts.


For Technical/General Support :

We assign you a dedicated counselor. If you find any difficulties or you want technical help, you can connect directly via WhatsApp.

Click here to jump to the top of the page. You just have to fill up the registration form and proceed with checkout.

You have to put 60 minutes a day for 60 days aprox.

Timings are flexible, you can study at your own time; daily around 60 minutes of content unlocks for you.

That’s totally fine, the course is designed to be completely flexible for all fields of study. It starts from complete beginner level.

You can get the curriculum PDF via WhatsApp, click on the “WhatsApp Us” button given below this section.

There is a part-payment feature that basically allows you to pay 500/- to book a seat and rest you can pay 1 week prior to the batch start date.


You can book it now through this link.
Part Payment Form Here

This program is completely online and there are no physical interactions or classroom visits required.

We are providing the complete training and internship program for a minimal fee. To maintain the fee, we cut our advertisement costs, this helps many financially weaker students to enroll and learn without the need of spending a fortune.

Please share the course with your connections to help us grow.

Data Science Course - Online Training and Internship

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!

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