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| Introduction to Data Science |
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Duration
Module 1 2 hrs
Introduction to Data Science
Data Science Era
Data Science involvement in Industries
Business Intelligence vs Data Science
Data Science Life Cycle
Tools of Data Science
Introduction to Python
Introduction to Machine Learning
Module 2 |
| Introduction to Python Programming |
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| Introduction to Python |
| Basic Operations in Python |
| Variable Assignment |
| Functions: in-built functions, user defined functions |
| Condition: if, if-else, nested if-else, else-if |
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Module 3 2 hrs |
| Data Structure – Introduction |
| List: Different Data Types in a List, List in a List |
| Operations on a list: Slicing, Splicing, Sub-setting |
| Condition(true/false) on a List |
| Applying functions on a List |
| Dictionary: Index, Value |
| Operation on a Dictionary: Slicing, Splicing, Sub-setting |
| Condition(true/false) on a Dictionary |
| Applying functions on a Dictionary
Modules and Packages |
| Numpy Array: Data Types in an Array, Dimensions of an Array 2 hrs |
| Operations on Array: Indexing ,Slicing, Splicing, Sub-setting |
| Conditional(T/F) on an Array |
| Loops: For, While |
| Shorthand for For |
| Conditions in shorthand for For
Control statements
Shape Manipulation
Linear Algebra |
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Module 4 2 hrs |
| Python Pandas – Home |
| Python Pandas – Introduction |
| Python Pandas – Environment Setup |
| Introduction to Data Structures |
| Python Pandas – Series |
| Python Pandas – DataFrame |
| Python Pandas – Panel |
| Python Pandas – Basic Functionality |
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| Function Application |
| Python Pandas – Reindexing |
| Python Pandas – Iteration |
| Python Pandas – Sorting |
| Working with Text Data |
| Options & Customization |
| Indexing & Selecting Data |
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| Python Pandas – Window Functions |
| Python Pandas – Aggregations 2 hrs |
| Python Pandas – Missing Data |
| Python Pandas – GroupBy |
| Python Pandas – Merging/Joining |
| Python Pandas – Concatenation |
| Python Pandas – Date Functionality |
| Python Pandas – Categorical Data |
| Python Pandas – Visualization |
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Module 5 2 hrs
Intro to Statistics
Statistical Inference
Terminologies of Statistics
Descriptive statistics
Statistical functions
Measures of Centers
Mean
Median
Mode
Measures of Spread
Variance
Standard Deviation
Histogram
Probability
Normal Distribution
Binary Distribution
Poisson distribution
Skewness
Bell curve
Hypothesis Building and Testing
Chi-Square Test
Correlation Matrix
Module 6 2 hrs
Scientific computing with Python
SciPy and its Characteristics
SciPy sub-packages
SciPy sub-packages –Integration
SciPy sub-packages – Optimize
Linear Algebra
SciPy sub-packages – Statistics
SciPy sub-packages – Weave
SciPy sub-packages – I O
Module 7 2 hrs
Data Analysis Pipeline
What is Data Extraction
Types of Data
Raw and Processed Data
Data Wrangling
Exploratory Data Analysis
Visualization of Data |
| MatplotLib |
| Bar Plot |
| Histogram Plot |
| Box Plot |
| Area Plot |
| Scatter Plot |
| Pie Plot
Seaborn |
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Module 8 2 hrs
Introduction to Machine Learning
Machine Learning Use-Cases
Machine Learning Process Flow
Machine Learning Categories |
| Module 9 |
| Data Preprocessing |
| Data preparation |
| Intro to Scikit Learn |
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Module 10 2 hrs |
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Regression |
| Types
Algorithms |
| Linear Regression
RMSE (Self Paced) 1hrs |
| Logistic Regression 2 hrs |
| Importance of Dimensions 2 hrs
Introduction to Dimensionality
Why Dimensionality Reduction
PCA
Factor Analysis
Scaling dimensional model
Encoding
Implementation with Case Studies
Intro to Kaggle and UCI repository
Module 11 2 hrs |
| Classification |
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| K-nearest neighbours
Metrics
Confusion Matrix
Classification report |
| Support Vector Machines 2 hrs |
| Working of SVM |
| Naive Bayes
Hyperparameter Optimization |
| Decision Tree Classifier
Random Forest Classifier
2 hrs
Ensemble Techniques and SVM tuning (Self Paced) 2 hrs
Underfitting & Overfitting |
| Entropy |
| AUC –ROC Curve (Self Paced) 1 hrs
Implementation with Case Studies
Cross –validation (Self Paced) 2 hrs
Module 12 2 hrs |
| Unsupervised learning |
| Clustering Algorithms
K-Means Clustering
Hierarchical Clustering |
| Implementation with Case Studies
Unsupervised Learning (Self Paced) 1 hrs
Module 13 2 hrs |
| NLTK Installation
Tokenize words
Tokenize sentences
Stop words in NLTK
Stemming words with NLTK
Speech tagging POS
ChatterBot (Self Paced) 1hrs |
| Web Scraping 2 hrs
Urllib
BeautifulSoup
Tf-idf Vectorizer |
| Sentiment analysis
Twitter Sentiment Analysis (Self Paced) 1hrs
Implementation with Case Studies
Pipelines (Self Paced) 1 hrs
Module 14 2 hrs
Recommendation Engine 2hrs
Collaborative filtering
Content Based Filtering |
| Implementation with Case Study
Surprise Library (Self Paced) 1 hrs
Introduction to Artificial Intelligence (Self Paced) 2hrs
Tensorflow library
Keras library
The course will be covering industrial real time case study .
At the end any one of the Capstone projects will be assigned.
Projects : 4 hrs
1. Customer churn prediction
2. Bank fraud Loan Prediction
3. Flight Price Prediction
4. Doctor Consultation Fee Prediction
5. Marketing Channel Sales Prediction
Course Highlights :
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| Use Case Based study |
| Hands on Live Projects
Study material
Self –Paced module
Interview questions
Mock Interview
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