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Batch Date: Dec 21st @ 8:30PM

Faculty: Mrs. Sasmitha

Duration: 45 Days

Venue :
DURGA SOFTWARE SOLUTIONS at Maitrivanam
Plot No : 202, IInd Floor ,
HUDA Maitrivanam,
Ameerpet, Hyderabad-500038.

Ph.No: +91 - 9246212143, 80 96 96 96 96

Syllabus:

DATA SCIENCE

Module - 1 (Python Basics)

Welcome To The Course

• What is Artificial Intelligence
• Introduction To DataScience
• Real Time UseCases Of DataScience
• Who is a DataScientist??
• DataScience Project Lifecycle
• Skillsets needed for DataScientist
• Difference between DataEngineer, DataScientist and DataAnalyst
• 6 Steps to take in 3.5 Months for a complete transformation to DataScience from any other domain
• Machine Learning-Giving Computers The ability to learn from data
• Supervised vs Unsupervised
• DeepLearning vs Machine Learning

Python Fundamentals

• Software Installation
• Jupyter Notebook Tutorial
• Introduction to Python
• Comments
• Variable,Operators,DataTypes
• If Else,For and While Loops
• Functions
• Lambda Expression
• Taking input from keyboard
• List
• Tuple
• Set
• Dictionary
• INTERVIEW QUESTIONS ASSIGNMENT-1

Module - 2 (Python Advance)

NumPy

• Introduction to Numpy
• Creating Arrays
• arange(),linspace() etc.
• Creating Arrays of Random Numbers
• Basic Operations on an Array
• Applying Universal functions on an array
• Linear Algebra operations on an array
• Numpy DataTypes
• Type Conversion
• Array Stacking
• ASSIGNMENT-2

Pandas

• Introduction to Pandas
• Creating DataFrames
• Reading data from csv,excel etc. into a DataFrame & writing df into csv,excel
• Selection and Indexing
• Conditional Selection
• Groupby
• Pivot Table
• Merging , Joining, Cancatenation
• Missing Value Treatment
• Data Visualisation using Pandas
• ASSIGNMENT-3

Module - 3 (Visualisation)

Visualisation-Plotly

• Line Plots
• Scatter Plots
• Pair Plots
• Histograms
• Heat Maps
• Bar Plots
• Count Plots
• Factor Plots
• Box Plots
• Violin Plots
• Swarm Plots
• Strip Plots
• Pandas Builtin Visualisation Library
• ASSIGNMENT-4

Module - 4 (Statistics)

Statistics

• Descriptive vs Inferential Statistics
• Mean,Median,Mode
• Central Limit Theorm
• Measure of dispersion
• Inter Quartile Range
• Variance
• Standard Deviation
• Box Plot
• Z score
• Scatter Plot
• Pearson’s Product Moment Correlation-r
• R square
• Adjusted R-square
• Normal Distribution
• Standard Normal Distribution
• Emprical rule of Normal Distribution
• What is an Outlier
• Outlier Detection and Removal

Module - 5 (ML-Linear Reg)

Linear Regression, Cost Function, Gradient Descent, Sklearn

• Introduction to Machine Learning
• Supervised vs Unsupervised
• Regression vs Classification
• Linear Regression Theory
• Gradients/Derivative Theory
• Assumption of Linear Regression
• Cost Function
• Optimize Cost function using Gradient Descent
• Gradient Descent Detail Explanation
• Mathematical Derivation
• Multi- Colinearity
• MAE
• MSE
• RMSE

Module - 6 (Decision Tree, Random Forest)

Decision Tree

• What is ID3 Algorithm
• Entropy
• Calculating Information Gain
• Overfitting, Underfitting, Best fit

Random Forest

• What is Bootstap
• Bagging
• Difference between Random Forest and Decision Tree
• Feature Selection using Random Forest
• Hyperparameter tuning

CLASSIFICATION VALIDATION TECHNIQUES

• Confusion Matrix
• Classification Report
• Recall
• Precision
• AUC
• ROC
• Cross Validation

Module - 7 (PCA)

Principal Component Analysis

• Introduction to Dimensionality Reduction
• PCA Theory discussion
• Eigen Values , Eigen Vectors
• Step by Step Detail Mathematical Derivation
• Individual and Cummulative Variation Ratio

Step By Step Implementation of PCA From scratch (with out sklearn) and by using sklearn

• Implement PCA from scratch using Numpy and using sklearn is a real time dataset

Module - 8 (KMeans)

KMeans Clustering

• Introduction to Unsupervised Machine Learning
• KMeans Theory
• How to decide K in KMeans

Module - 9 (NLP)

Text Mining

• Introduction to NLP
• Text Preprocessing Techniques using Space and NLTK
• Word Tokens
• StopWord Removal
• Count Vectorizer
• Tf-Idf Vectorizer