Data Science Course in Hyderabad

• What is data science?
• What do data scientists do?
• Applications of data science 
• History of data science 
• Basics of data science

Projects/assessment :

1. Know more about data science

• Tools used for data science 
• Importance of each tool 
• Tool use case

Installation of the required tools

Introduction to python programming

• Python Data Structures
• Python Programming Fundamentals
• Conditions and Branching
• Loops
• Functions
• Python Packages 

Python Libraries

• Working with NUMPY
• Working with Pandas
• How to read data using pandas
• Introduction to Matplotlib and Seaborn
• Basic Plotting with Matplotlib and Seaborn
• EDA Techniques in Python

Assessment 1: 

Find factorial of a number using python

Skills: Basic Python

Assessment 2: 

Work on Startup Data- Perform various visualisation & Statistical techniques using pythonSkills: Python Libraries

Descriptive statistics: 

• Types of Data
• Various Classifications of Data
• Balanced vs Imbalanced datasets
• Various sampling techniques for handling balanced vs imbalanced datasets

• Sampling Funnel

• Population
• Sampling frame
• Simple random sampling
• Sample

• Measure of central tendency

• Mean /Average
• Median
• Mode

• Measure of Dispersion

• Variance
• Standard Deviation
• Range

• Measure of Skewness
• Measure of Kurtosis
• Quantiles
• Normal Distribution
• Standard Normal Distribution / Z distribution
• Z scores and Z table

Inferential Statistics:

• Central Limit Theorem
• Probabilities

Hypothesis Testing

• Parametric vs Non-parametric tests
• Formulating a Hypothesis
• Choosing Null and Alternative hypothesis
• Type I and Type II errors
• Comparative study of sample proportions using Hypothesis testing
• 2 sample t test
• 1 sample z test
• ANOVA

Assessment 3: 

Perform Statistical analysis to sample data and get insights

Skills: Basic Statistical analysis

Assessment 4: 

Work on the same data above and get insights.Skills: Advanced Statistical analysis

• Various graphical techniques to understand data
• Bar plot
• Histogram
• Box plot
• Scatter plot
• QQ Plot /Quantile-Quantile plot
• Scatter plot
• Pair plot
• Correlation-Heatmap

Assessment 5: 

Work on the same data above and get insights.

Skills: Matplotlib & Seaborn

• Basic Understanding of Data
• Treating Missing values
• Working with Continuous and Categorical Variables
• Visual Analysis

Data Manipulation

• Feature Engineering

Project 1 : 

Work on Startups Data and perform EDA & Feature Engineering Techniques

Skills: EDA & Feature Engineering Techniques

• Introduction to machine learning
• Importance of ML
• Knowing libraries used for machine learning in python (Scikit-learn)

Supervised machine learning

Regression

• Linear regression
• Multiple linear regression
• Lasso and ridge regression

Classification

• Logistics regression
• KNN
• Navie Bayes
• SVM
• Decision tree
• Gradient boosting
• XG boost and ada boost

Validation and evaluation and models UnSupervised learning

• Introduction to UnSupervised learning
• Clustering
• K-means clustering
• Hierarchical clustering

Project 2 : 

Predict profit of a startup company

Skills: Regression analysis

Project 3 : 

Survival Analysis – Diabetes Prediction ( Apply all algorithm’s and find best accurate model )

Skills: Classification

• Applying validation, Evaluation on projects 2 & 3

Project 4: 

Work on Mall Customers data and cluster the data.

Skill: Clustering

• Introduction to Deep Learning
• Why Deep Learning
• Machine Learning vs Deep Learning
• Applications of Deep Learning
• Knowing Libraries used for DeepLearning (Tensorflow, Keras)

Introduction to Neural Networks

• The Neuron
• The Activation Function
• How do Neural Networks work?
• How do Neural Networks learn?
• Gradient Descent
• Stochastic Gradient
• Descent
•  Backpropagation
•  Forward propagation

ANN:

•  Building ANN Using Keras
• Framework

CNN

• What is CNN?
• Convolution Operation
• Pooling
• Flattening
• Full Connection
• Classification of fruits using CNN

Introduction to Recurrent Neural Networks

• The Concept of Recurrent Neural Networks
• The Vanishing Gradient Problem
• LSTMs
• LSTM Variations
• Predicting Google
• stock prices using
• RNN

Project 5: 

Employee Churn Prediction

Skills: ANN, Keras, Tensorflow

Project 6: 

Build a gesture recognition system

Skills: CNN

Project 7: 

Predictive maintenance for airlines

Skills: RNN

• Introduction to Opencv
• Modules
• Haar Cascade classifiers
• Object detection
• Contours
• YOLO

Project 8: 

Detecting tables and graphs in pdf files

Skills: Open CV

Introduction to natural language processing

• What is NLP
• Web scraping
• Text mining
• Working with the NLTK
• Preprocessing text data
• Bag of words model 
• Sentiment analysis using NLP

Project 9:

Restaurant reviews analysis

Skills: NLP

SQL

• Introduction to database
• Database management system 
• Introduction to SQL
• Working with My SQL 
• Perform CRUD operations using My SQL

Tableau

• Introduction to Tableau
• Working with worksheets 
• Filters
• charts

Project 10 (a)

Perform CRUD operations with My SQL

Skills: SQL

Project 10 (b)

Perform analysis on bank data using tableau

Skills: Tableau