| Data Science with Machine Learning |
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| ( Professional Course ) |
Time : 40 hrs |
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| Become a Data Scientist |
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| Data science is the new emerging field in today world with high growth and career perspective. It’s right time to move your career in data science by starting from basics of Data analytics and management to advanced topics like in Machine Learning with industrial case study. This course provides in-depth understanding of how data science integrates in various industrial verticals such as healthcare, banking, telecom, e-commerce, transportation and more. |
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| Introduction to Data Science |
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| Duration |
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| Module 1 |
2 hrs |
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| Introduction to Data Science |
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| Data Science Era |
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| Data Science involvement in Industries |
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| Business Intelligence vs Data Science |
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| Data Science Life Cycle |
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| Tools of Data Science |
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| Introduction to Python |
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| Introduction to Machine Learning |
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| Module 2 |
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| Introduction to Python Programming |
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| Introduction to Python |
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| Basic Operations in Python |
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| Variable Assignment |
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| Functions: in-built functions, user defined functions |
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| Condition: if, if-else, nested if-else, else-if |
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| Module 3 |
2 hrs |
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| Data Structure – Introduction |
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| List: Different Data Types in a List, List in a List |
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| Operations on a list: Slicing, Splicing, Sub-setting |
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| Condition(true/false) on a List |
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| Applying functions on a List |
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| Dictionary: Index, Value |
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| Operation on a Dictionary: Slicing, Splicing, Sub-setting |
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| Condition(true/false) on a Dictionary |
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| Applying functions on a Dictionary |
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| Modules and Packages |
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| Numpy Array: Data Types in an Array, Dimensions of an Array |
2 hrs |
| Operations on Array: Indexing ,Slicing, Splicing, Sub-setting |
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| Conditional(T/F) on an Array |
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| Loops: For, While |
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| Shorthand for For |
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| Conditions in shorthand for For |
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| Control statements |
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| Shape Manipulation |
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| Linear Algebra |
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| Module 4 |
2 hrs |
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| Python Pandas – Home |
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| Python Pandas – Introduction |
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| Python Pandas – Environment Setup |
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| Introduction to Data Structures |
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| Python Pandas – Series |
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| Python Pandas – Data Frame |
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| Python Pandas – Panel |
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| Python Pandas – Basic Functionality |
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| Function Application |
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| Python Pandas – Reindexing |
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| Python Pandas – Iteration |
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| Python Pandas – Sorting |
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| Module 5 |
2 hrs |
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| Intro to Statistics |
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| Statistical Inference |
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| Terminologies of Statistics |
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| Descriptive statistics |
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| Statistical functions |
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| Measures of Centers |
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| Mean |
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| Median |
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| Mode |
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| Measures of Spread |
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| Variance |
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| Standard Deviation |
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| Histogram |
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| Probability |
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| Normal Distribution |
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| Binary Distribution |
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| Poisson distribution |
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| Skewness |
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| Bell curve |
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| Hypothesis Building and Testing |
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| Chi-Square Test |
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| Module 6 |
2 hrs |
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| Scientific computing with Python |
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| SciPy and its Characteristics |
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| SciPy sub-packages |
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| Linear Algebra |
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| SciPy sub-packages – Statistics |
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| SciPy sub-packages – I O |
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| Module 7 |
2 hrs |
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| Data Analysis Pipeline |
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| What is Data Extraction |
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| Types of Data |
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| Raw and Processed Data |
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| Data Wrangling |
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| Exploratory Data Analysis |
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| Visualization of Data |
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| MatplotLib |
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| Bar Plot |
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| Histogram Plot |
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| Box Plot |
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| Area Plot |
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| Scatter Plot |
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| Pie Plot |
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| Seaborn |
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| Module 8 |
2 hrs |
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| Introduction to Machine Learning |
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| Machine Learning Use-Cases |
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| Machine Learning Process Flow |
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| Machine Learning Categories |
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| Module 9 |
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| Data Preprocessing |
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| Data preparation |
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| Intro to Scikit Learn |
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| Module 10 |
2 hrs |
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| Regression |
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| Types |
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| Algorithms |
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| Linear Regression |
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| Logistic Regression |
2 hrs |
| Importance of Dimensions |
2 hrs |
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| Introduction to Dimensionality |
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| Why Dimensionality Reduction |
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| PCA |
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| Factor Analysis |
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| Scaling dimensional model |
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| Implementation with Case Studies |
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| Intro to Kaggle and UCI repository |
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| Module 11 |
2 hrs |
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| Classification |
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| K-nearest neighbours |
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| Metrics |
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| Confusion Matrix |
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| Classification report |
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| Support Vector Machines |
2 hrs |
| Working of SVM |
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| Naive Bayes |
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| Hyperparameter Optimization |
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| Decision Tree Classifier |
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| Entropy |
2 hrs |
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| Gini Entropy |
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| ROC |
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| AUC |
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| Random Forest classifier |
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| Linear Discriminant Analysis |
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| Cross –validation |
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| Implementation with Case Studies |
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| Module 12 |
2 hrs |
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| Unsupervised learning |
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| Clustering Algorithms |
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| K-Means Clustering |
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| Hierarchical Clustering |
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| Implementation with Case Studies |
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| Module 13 |
2 hrs |
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| NLTK Installation |
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| Tokenize words |
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| Tokenize sentences |
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| Stop words in NLTK |
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| Stemming words with NLTK |
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| Speech tagging |
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| Beautiful Soup |
2 hrs |
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| Tf-idf Vectorise |
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| Sentiment analysis |
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| Implementation with Case Studies |
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| Module 14 |
2 hrs |
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| Introduction to Artificial Intelligence |
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| Introduction to Tensorflow |
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| Implementation with MNIST Case Study |
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| At the End implementation of any one of the projects could be assigned. |
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| Projects : |
4 hrs |
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| 1. Customer churn prediction |
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| 2. Bank fraud Loan Prediction |
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| 3. Wine Type Prediction |
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| 4. Titanic dataset |
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| 5. Marketing channel sales prediction |
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| Course Highlights : |
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| Use Case Based study |
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| Hands on Live Projects |
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| Study material |
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| Interview questions |
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| Mock Interview |
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