Orientation & Introduction to Data Science
2
LinkedIn Profile Update
3
Journey Towards Data Science
Python Programming
1
Introduction to Python
2
Anaconda – Installation
3
Hello World with Python
4
Variables and Data Types in Python
5
Project 1 : Student Portfolio
6
Strings and Lists in Python
7
Conditional Statements in Python
8
Project 2 : Rock Paper Scissor
9
Iterations in Python – For and While Loop
10
Methods & Functions
11
Project 3 : Multiplication Tables
12
Project 4 : Real Calculator
14
How’s the course so far ?
15
Object Oriented Programming in Python
16
Graphical User Interface with Tkinter
17
Project 5 – News App with Tkinter : Part 1
18
Project 5 : News App with Tkinter : Part 2
19
Project 5 – News App with Tkinter : Part 3
20
Project 6 : Bulk File Renamer
21
Bulk File Renamer Error Resolving
Batch Leader Selections
1
Batch Leader – Video Task (Optional)
NumPy and Pandas
1
Computing with NumPy
2
Introduction to Pandas
3
Dataframes
4
Reindexing & Working with Text Data
5
Invalid Missing Data and GroupBy
6
Merging and Concatenation
7
Time Date Functionality & Categorical Data
8
Project 7 – Real World Financial Data Analysis
R Programming for Statistics & Data Science
1
Introduction, Installation and R Operators
2
Datatypes in R
3
Variables in R
4
Control Flow – If Else and Loops
5
Decision Making in R
6
Project 8 – Simple Calculator with R
7
Recursive and Conversion Functions
8
Data Structures in R
9
Object Oriented Programming – OOPs
10
Error Handling in R
11
Introduction to File Handling
12
File Handling – Continued
13
Data Interfaces
Advanced Statistics and Probability with R
1
Statistics and Data
2
Head First Statistics – Book by O’Reilly
3
Measures of Central Tendency in R
4
Measures of Dispersion in R
5
Measure of Relationship Between Variables
6
MAE, MSE, RMSE and R-Squared
7
Important Commands for Descriptive Stats
8
Inferential Statistics & Hypothesis Testing
9
Bootstrapping
10
ANOVA
11
Normal Distribution
12
Binomial Distribution
13
Basic Probability
14
Skewness & Kurtosis
15
Chi-Square Test
16
Regression
17
Survival Analysis
A Precursory Glance into the World of Machine Learning
1
Uses of Machine Learning
2
Linear Regression
3
Logistic Regression
Machine Learning Essentials
1
Introduction to Machine Learning
2
Linear Regression Model
3
Classification Model
4
Classification Metrics in Depth
5
Statistics – Quick Revision
6
Central Limit Theorem and Decision Trees
7
Ensemble Model and Clustering
8
Project 8 – Car Price Prediction – Part 1
9
Project 8 – Car Price Prediction – Part 2
10
ANN & CNN
11
Introduction to Google Colab
12
Project 9 – Music Preference Predictor
13
Project 10 – Credit Card Defaulters Prediction
14
Recurrent Neural Network – RNN
Data Visualization with Python and R
1
Data Visualization – Introduction
2
Basic Data Visualization in R
3
Basic Visualization in R – Continued
4
Advanced Data Visualization in R
5
Data Visualization in Python – Matplotlib
6
Data Visualization in Python – Seaborn
7
Seaborn – Continued
8
Project 11 – Covid Statistics Tracker
SQL for Data Science
1
Introduction and Significance of SQL
2
Data and SQL, MySQL Workbench Installation
3
SQL Statements and Constraints
4
SQL Keys, Datatypes and Case Manipulation
5
Character Manipulation
6
Numeric Functions
7
SQL Joins
8
Basic SQL Queries
9
Python and SQL
10
Project 12 – Corporate Assignment 1
11
Project 12 – Corporate Assignment 2
12
Project 12 – Corporate Assignment 3
13
Project 12 – Corporate Assignment 4
14
Project 12 – Corporate Assignment 5
Final Steps
1
CF-DORI : Final Exam
2
Placement & Certificate Form
CFT Placement Prep (To be Uploaded)
For doubts support . On lesson page in course dashboard, students have a QnA section 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 with their mentors.
No boundaries on field of study or age etc. Enthusiasts interested in learning Data Science whether to change career at a later age or to start career at a young age are all welcome.
Candidates can join if they have a minimum age 16 and understands English well.
Yes, we start from absolute basics. The training is designed for beginners and moderate level students and hence we start each topic from beginner level.
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, typing, etc. and add transitions for better visibility.
The following modules have been redesigned:
R Programming, Advanced Statistics, Data Visualization, Placement Prep.
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