Advanced Diploma In Data Science
Description
Tools Covered
- Python (Anaconda Jupyter Notebook)
- R (Optional)
- Excel
- Tableau/Power BI
- SQL
Subjects
- Introduction to Data Science
- What is Data Science?
- What is Machine Learning?
- What is Deep Learning?
- What is AI?
- Data Analytics & it’s types
- Introduction to Python
- What is Python?
- Why Python?
- Installing Python
- Python IDEs
- Advanced Python Techniques Implementation in Data Science
Python Basics
- Python Basic Data types
- Lists
- Slicing
- IF statements
- Loops
- Dictionaries
- Tuples
- Functions
- Array
- Selection by position & Labels
- Python modules
Data Wrangling with Python
- Data munging techniques
- Data Acquisition (Import & Export)
- Indexing
- Selection and Filtering Sorting & Summarizing
- Descriptive Statistics
- Combining and Merging Data Frames
- Removing Duplicates
- Discretization and Binning
- String Manipulation
- Exploratory Data Analysis (EDA)
- Why EDA?
- Processes in EDA
- Univariate and Bivariate Analysis
- Descriptive Statistics
- Data Cleaning
- Transformation
- Missing Value Analysis
- Outlier Detection Analysis
- Featured Engineering& Feature Selection
- Data Manipulation with Excel (Optional)
- How to find insights from data?
- Data Visualization
- Design principles &Grammar of Graphics
- Data Visualization with Python (Matplotlib & Seaborn)
- Tableau/Power BI
- Statistics
- Measures of central tendency
- Measures of variability
- Skewness and Kurtosis
- Normal distribution
- Central limit theorem
- Confidence interval
- T-test
- Type I and II errors
- Student’s T distribution
- ANOVA
- P-value
- Correlation Analysis
- Hypothesis Testing
- SQL
- Introduction to SQL
- DDL & DML Statement
- SELECT Statement
- AGGREGATE functions
- WHERE, ORDER BY, DISTINCT, GROUP BY, LIKE, AND & OR clause
- UPDATE & DELETE query
- JOINS
- UNION, UNION ALL, INTERSECT
- Sub Queries
- NULL values & DATE function
- Machine Learning
- Supervised and Unsupervised Learning
- Regression and Classification Models
- Linear Regression with Python
- Logistic Regression with Python
- Tree-based algorithms – Decision Tree, Random Forest
- Ensemble Methods
- Clustering – K-Means, Hierarchical Clustering
- K-Nearest Neighbours
- Dimensionality Reduction Techniques – PCA, Factor Analysis
- Time Series Analysis
- Bagging and Boosting
- Performance Measures
- Bias-Variance Trade-Off
- Optimization Techniques
- Overfitting & Underfitting
- Capstone Project
Note: Bonus Session – How to prepare for interviews? Resume Preparation Tips
