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FULL STACK DATA SCIENCE Course Details
 

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Batch Date: June 14th @ 8:00PM

Faculty: Mrs. Sasmitha

Duration: 75 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:

Full Stack Data Science Program
in Artificial Intelligence, Machine Learning and Deep Learning

Program Details

Python

• Python Installation
• Jupyter Notebook Tutorial
• Variable
• Function
• Lambda Expression
• Loops
• List
• Tuple
• Set
• Dictionary
• Coding Test-1
• Assignment-1
• Assignment-2
• Assignment-3

Advance Python

• Introduction to Numpy
• Creating Arrays
• Selection and Indexing
• Basic Operations on Arrays
• Mathematical Operation on Arrays
• Linear Algebra Operation on Arrays
• Stacking Arrays
• Data Types and Type Conversion
• Assignment-4
• Introduction to Pandas
• Creating Data Frames
• Reading and Writing Operation
• Selection and Indexing
• Conditional Selection
• Assignmet-5
• Groupby
• Pivot Table
• Merge
• Join
• Concat
• Assignment-6
• Missing Value Treatment
• Drop Duplicates
• Dealing with Date Time Data
• Apply()
• Introduction to Series
• Series Operation
• Pandas Builtin Functions for Data Visualisation
• Assignment-7
• Coding Test-2

Visualisation

• Introduction To Plotly
• Scatter Plot
• Line Plot
• Scatter Matrix
• Box Plot
• Bar Chart
• Histogram
• Sun Burst Chart
• Create DashBoard

Statistics

• Central Limit Theorem
• Measure of Dispersion
• Quartiles
• Inter Quartile Ranges
• Variance
• Standard Deviation
• Z Score
• Normal Distribution
• Pearson Correlation Coefficient- R
• R Square
• Adjust R2
• Multi Colinearity Detection Techniques
• Multi Colinearity Removal Techniques
• Outliers Detection and Removal
• Assignment-8

Machine Learning

• Introduction to Machine Learning
• Difference Between Supervised & Unsupervised Learning
• Difference Between Classification and Regression
• Machine Learning Application
• Data Science Project Life Cycle
• Linear Regression

• Theory of Linear Regression
• Cost Function
• Optimization Using Gradient Descent
• Mathematical Interpretation of Gradient Descent
• Project-1 – Sales Prediction Project
• Understanding Why Linear Regression may fail?
• Model Validation Techniques

• Mean Squared Error
• Root Mean Squared Error
• Mean Absolute Error

• Polynomial Regression

• Understanding Polynomial Regression
• Implementing Polynomial Regression Using Python
• Overfitting, Underfitting, Right Fit
• Coding Test- 2- Project-2 (Finance project)

• Logistic Regression

• Understanding Logistic Regression Step by Step
• Project-3 – Retail Project

• Decision Tree and Random Forest

• ID3 Algorithm vs CART
• Entropy
• Information Gain
• Step by Step Understanding of How Decision Tree Work
• How to overcome overfitting in DT
• Cross Validation
• Bootstrap Aggregation/Bagging
• Introduction to Random Forest
• How Random Forest Works
• Feature Selection
• Model Validation Techniques

• Accuracy
• Confusion Matrix
• Classification Report
• Recall
• Precision

• Project-4- Healthcare Project
• Coding Test-5 – Project-5(Banking Project)
• Hyper parameter Tuning

• KMeans Clustering

• What is Euclidian Distance
• Introduction to Unsupervised Learning
• Step By Step Mathematical Derivation
• Pros and Cons Of K Means
• Elbow Method to Find K
• Project-6- Customer Segmentation

Deep Learning

• What is Deep Learning
• Deep Learning VS Machine Learning
• What is a Perceptron
• How Neural Network Learns
• Multi Layer Perceptron
• Activation Function
• Introduction to Keras
• What is Feed Forward Network
• Detail Explanation of ANN
• What is Cost Function
• Optimization Technique
• Vanilla Gradient Descent
• Mini Batch Gradient Descent
• Stochastic Gradient Descent
• Softmax
• Cross Entropy Loss
• MSE vs Cross Entropy
• Project-7 - Price Prediction Project
• Projet-8- Coding Test- Classification Project(IOT Data- Aviation Domain)

Image Processing , CNN & Computer Vision

• Introduction to Computer Vision
• Challenges in Computer Vision
• Introduction to Open CV
• Image Basics
• Reading and Writing Images/Videos
• Rescaling / Normalisation
• Color Mapping
• Thresholding of an Image
• Morphological Transformation
• Image Augmentation Using Keras
• What is Image Filters
• Different Kind of Filters
• Convolution
• What is Convolutional Neural network
• Pooling
• Overfitting In Deep Learning
• Drop Outs
• Project-9- X-ray Image Classification(HealthCare)

Time Series Analysis

• What is Time Series Data
• Resampling
• Time Shifting
• Interpolation
• Missing Value Treatment in Time Series
• Trend
• Seasonality
• Auto Correlation
• Time Series Decomposition
• Moving Average
• Exponential Moving Average
• Time Series Modelling Using Facebook Prophet
• Project-10- Time Series Forecasting Project

Natural Language Processing-Text Mining

• What is Unstructured Data
• Introduction to NLTK and Spacy
• Tokenization
• Stop Word Removal
• Stemming
• Lemmatization
• N-Grams
• What is Word Embedding
• Count Vectorizer
• Tf-Idf Vectorizer
• Pattern Matching
• Regular Expression
• Project-11 – Sentiment Analysis(Social Media Data)
• Project-12- Document Clustering (News Data)

Big Data Analytics - Apache Spark

• Introduction to Apache Spark
• Parallel vs Distributed Computing
• Introduction to Big Data
• Spark Installation
• Spark Vs Hadoop
• Spark Architecture
• Lazy Evaluation
• RDD
• Spark SQL & DataFrame
• Spark ML Lib
• Project-13- Retail Domain Project using Spark MLLib