About Profound Data Science Classes?

According to the survey data scientist is the top ranking professional in the market. As a Data Scientist you need to understand the Business problem, gather data for the same and analyze by applying correct algorithms and techniques using current tools. A after running data through the Model you come out with the result of the exercise which include visualizing the result and communicates results to concern people in form of powepoint or some sort of report.

Need of Data Science

- Data Science is basically used to take better decision.
For example, whether to buy Self Driven Car or not. Current Data analysis shows it will root out more than 2 million deaths caused due to car accidents

- Predictive Analysis
What Will Happen Next

- Pattern Discovery
Is there any hidden information in the data. For example, to analyse patterns of sales that is, in which month it is increasing

What is Data Science?

- Asking the right Questions and Exploring the data
Basically, you need to know what problem you are solving that is asking the right question. After asking questions you will have data for that as input and you will perform certain action on it

- Modeling the data using various algorithms
Suppose if you need to perform machine learning you have to decide which Model to use and which algorithms to use and then you need to train the model and so on.

- Finally Communicating and Visualizing the results.
After running the data through the Model you come out with the result of the exercise which include visualizing the result and communicates results to concern people in form of powepoint or some sort of report. All the information that has been gathered

Why learn Data Science course at Profound Edutech?

- Data Science Course at profound is designed to train students and professional in the industry's most widely sought after skills, and make them job ready in the field of Data Science.

- We at profound Edutech provide you with an excellent platform to learn and explore the subjects from Industry experts.

- 100 % placement assistance for the trained candidates.

- Training by experienced faculties

Duration: 5 months

Eligibility: IT Professionals / Exposure to Information Technology

Data Science Fundamentals

What is Data Science?
Why its important?
Life Cycle of Data Science
Role of a Data Scientist
Difference between Data Analytics & Data Science

Essential Statistics

Probability
Introduction
Why do we need to learn Probability and Statistics?
The Basic Probability Formula
Computing Expected Values
Frequency
Events and Their Complements
Probability - Combinatorics
Fundamentals of Combinatorics
Permutations and How to Use Them
Simple Operations with Factorials
Solving Variations with Repetition
Solving Variations without Repetition
Solving Combinations
Symmetry of Combinations
Solving Combinations with Separate Sample Spaces
Combinatorics in Real-Life: The Lottery
Probability - Bayesian Inference
Sets and Events
Intersection of Sets
Union of Sets
Mutually Exclusive Sets
Dependence and Independence of Sets
The Conditional Probability Formula
The Law of Total Probability
The Additive Rule
The Multiplication Law
Bayes' Law
A Practical Example of Bayesian Inference
Probability - Distributions
Fundamentals of Probability Distributions
Types of Probability Distributions
Characteristics of Discrete Distributions
Discrete Distributions: The Uniform Distribution
Discrete Distributions: The Bernoulli Distribution
Discrete Distributions: The Binomial Distribution
Discrete Distributions: The Poisson Distribution
Characteristics of Continuous Distributions
Continuous Distributions: The Normal Distribution
Continuous Distributions: The Standard Normal Distribution
Continuous Distributions: The Students' T Distribution
Continuous Distributions: The Chi-Squared Distribution
Continuous Distributions: The Exponential Distribution
Continuous Distributions: The Logistic Distribution
A Practical Example of Probability Distributions

Descriptive Statistics

Types of Data
Levels of Measurement
Categorical Variables - Visualization Techniques
Numerical Variables - Frequency Distribution Table
The Histogram
Cross Tables and Scatter Plots
Mean, median and mode
Skewness
Variance
Standard Deviation and Coefficient of Variation
Covariance
Correlation Coefficient
Statistics - Inferential Statistics
Introduction
What is a Distribution
The Normal Distribution
The Standard Normal Distribution
Central Limit Theorem
Standard error
Estimators and Estimates
Statistics - Inferential Statistics: Confidence Intervals
What are Confidence Intervals?
Confidence Intervals; Population Variance Known; Z-score
Confidence Interval Clarifications
Student's T Distribution
Confidence Intervals; Population Variance Unknown; T-score
Margin of Error
Confidence intervals. Two means. Dependent samples, Independent Samples
Statistics - Hypothesis Testing
Null vs Alternative Hypothesis
Further Reading on Null and Alternative Hypothesis
Null vs Alternative Hypothesis
Rejection Region and Significance Level
Type I Error and Type II Error
Test for the Mean. Population Variance Known
p-value
Test for the Mean. Population Variance Unknown, Dependent Samples, Independent Samples

Essential Mathematics

What is a Matrix?
Scalars and Vectors
Linear Algebra and Geometry
Arrays in Python - A Convenient Way To Represent Matrices
Addition and Subtraction of Matrices
Errors when Adding Matrices
Transpose of a Matrix
Dot Product
Dot Product of Matrices
Why is Linear Algebra Useful?

Introduction to Data Analysis Using Spreadsheet Excel

Perform basic spreadsheet tasks
- viewing, entering and editing data, and moving, copying
Cleaning & wranggling data
- remove duplicate / inaccurate data / empty rows, manipulate & standardize data
Conditional Formatting
Functions
- CONCATINATE, LEN, TRIM, COUNTA, AGGREGATEIFS, SUMIF, COUNTIF
Fundamentals of analyzing data using filter and sort data.
Pivot Tables
HLOOKUP, VLOOKUP
What-if analysis
Charts
Descriptive Statistics - Annova, Regression

Mini Project on Data Analytics

Database & SQL

Create DB, Drop DB
Create Table, Drop Table, Alter Table
Data Types
Constraints, Not Null, Unique
Primary Key, Foreign Key
Create Index
Dates
Views
Summarizing results using group functions
Joins
Retrieving Data With Sub Queries
Manipulating Data

Core Python For Data Science

Introduction to Python Programming
Installation & working
Basic Operators, Data types, Variables
Control Statements & Conditional Looping
Functions
Collections in Python
Object Oriented Programming
Modules and Packages
String
File Handling
Exception Handling

Advance Python (Data Analysis)

NumPy
Introduction, installation
1D & 2D arrays
Array indexing – slicing & advance
Operations – Arithmetic, Logical, Math, String, Statistical, Set, Broadcasting
Pandas
Introduction, installation
Series – Creation, indexing, slicing, attributes & functions
Dataframes – Creation, operations, merging dataframes, Concatenate dataframes, binary operations
Data input and output