Data Science Course with Online Internship.

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
  • Pandas – Pandas 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

1. • 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.