Advanced Certification in Data Science & Data Engineering

1. Python

• Introduction to Python and IDEs – The basics of the python programming language, how you can use various IDEs for python development like Jupyter, Pycharm, etc.
• Python Basics – Variables, Data Types, Loops, Conditional Statements, functions, decorators, lambda functions, file handling, exception handling, etc.
• Object Oriented Programming – Introduction to OOPs concepts like classes, objects, inheritance, abstraction, polymorphism, encapsulation, etc.
• Hands-on Sessions and Assignments for Practice – The culmination of all the above concepts with real-world problem statements for better understanding.

2. Linux

• Introduction to Linux – Establishing the fundamental knowledge of how linux works and how you can begin with Linux OS.
• Linux Basics – File Handling, data extraction, etc.
• Hands-on Sessions and Assignments for Practice – Strategically curated problem statements for you to start with Linux.

• Extract Transform Load
  • Web Scraping, Interacting with APIs [Ma1]

• Data Handling with NumPy
  • NumPy Arrays, CRUD Operations,etc.
  • Linear Algebra – Matrix multiplication, CRUD operations, Inverse, Transpose, Rank, Determinant of a matrix, Scalars, Vectors, Matrices.

• Data Manipulation Using Pandas
  • Loading the data, dataframes, series, CRUD operations, splitting the data, etc.

• Data Preprocessing
  • Exploratory Data Analysis, Feature engineering, Feature scaling, Normalization, standardization, etc.
  • Null Value Imputations, Outliers Analysis and Handling, VIF, Bias-variance trade-off, cross validation techniques, train-test split, etc.

• Scientific Computing with Scipy
  • Introduction to scipy, building on top of numpy
  • What are the characteristics of scipy?
  • Various subpackages for scipy like Signal, Integrate, Fftpack, Cluster, Optimize, Stats and more, Bayes Theorem with scipy.

• Hands-on Exercise:
  • Importing of scipy
  • Applying the Bayes theorem on the given dataset.

• Data Visualization
  • Bar charts, scatter plots, count plots, line plots, pie charts, donut charts, etc, with Python matplotlib.
  • Regression plots, categorical plots, area plots, etc, with Python seaborn.

• Introduction to Machine learning
  • Supervised, Unsupervised learning.
  • Introduction to scikit-learn, Keras, etc.

• Supervised Learning
  • Regression – Introduction classification problems, Identification of a regression problem, dependent and independent variables. How to train the model in a regression problem. How to evaluate the model for a regression problem. How to optimize the efficiency of the regression model.
  • Classification – Introduction to classification problems, Identification of a classification problem, dependent and independent variables. How to train the model in a classification problem. How to evaluate the model for a classification problem. How to optimize the efficiency of the classification model[Ma5]
  • Linear Regression – Creating linear regression models for linear data using statistical tests, data pre-processing, standardization, normalization, etc.
  • Logistic Regression – Creating logistic regression models for classification problems – such as if a person is diabetic or not, if there will be rain or not, etc.
  • Decision Tree – Creating decision tree models on classification problems in a tree like format with optimal solutions.
  • Random Forest – Creating random forest models for classification problems in a supervised learning approach.
  • Support Vector Machine – SVM or support vector machines for regression and classification problems.
  • Gradient Descent – Gradient descent algorithm that is an iterative optimization approach to finding local minimum and maximum of a given function.
  • K-Nearest Neighbors – A simple algorithm that can be used for classification problems.
  • Time Series Forecasting – Making use of time series data, gathering insights and useful forecasting solutions using time series forecasting.

• Unsupervised Learning
  • Clustering – Introduction to clustering problems, Identification of a clustering problem, dependent and independent variables, How to train the model in a clustering problem, How to evaluate the model for a clustering problem, How to optimize the efficiency of the clustering model.
  • K-means – The k-means algorithm that can be used for clustering problems in an unsupervised learning approach.
  • Dimensionality reduction – Handling multi-dimensional data and standardizing the features for easier computation.
  • Principal Component Analysis – PCA follows the same approach in handling the multidimensional data.
  • Linear Discriminant Analysis – LDA or linear discriminant analysis to reduce or optimize the dimensions in the multidimensional data.
  • Association Rule Mining – Identifying strong rules in the data using machine learning.
  • Apriori Algorithm – For finding frequent itemsets in a dataset.

• Performance Metrics
  • Classification reports – To evaluate the model on various metrics like recall, precision, f-support, etc.
  • Confusion matrix – To evaluate the true positive/negative, false positive/negative outcomes in the model.
  • r2, adjusted r2, mean squared error, etc.

1. Non-Relational Data Stores and Azure Data Lake Storage

1.1 Document data stores
1.2 Columnar data stores
1.3 Key/value data stores
1.4 Graph data stores
1.5 Time series data stores
1.6 Object data stores
1.7 External index
1.8 Why NoSQL or Non-Relational DB?
1.9 When to Choose NoSQL or Non-Relational DB?
1.10 Azure Data Lake Storage

Definition, Azure Data Lake-Key Components, How it stores data? Azure Data Lake Storage Gen2, Why Data Lake? Data Lake Architecture

2. Data Lake and Azure Cosmos DB

2.1 Data Lake Key Concepts
2.2 Azure Cosmos DB
2.3 Why Azure Cosmos DB?
2.4 Azure Blob Storage
2.5 Why Azure Blob Storage?
2.6 Data Partitioning: Horizontal partitioning, vertical partitioning, Functional partitioning
2.7 Why Partitioning Data?
2.8 Consistency Levels in AzureCosmos DB:  Semantics of the five-consistency level

3. Relational Data Stores

3.1 Introduction to Relational Data Stores
3.2 Azure SQL Database – Deployment Models, Service Tiers
3.3 Why SQL Database Elastic Pool?

4. Why Azure SQL?

4.1 Azure SQL Security Capabilities
4.2 High-Availability and Azure SQL Database: Standard Availability Model, Premium Availability Model
4.3 Azure Database for MySQL
4.4 Azure Database for PostgreSQL
4.5 Azure Database for MariaDB
4.6 What is PolyBase and Why PolyBase?
4.7 What is Azure Synapse Analytics (formerly SQL DW): SQL Analytics and SQL pool in Azure Synapse, Key component of a big data solution, SQL Analytics MPP architecture components

5. Azure Batch

5.1 What is Azure Batch?
5.2 Intrinsically Parallel Workloads
5.3 Tightly Coupled Workloads
5.4 Additional Batch Capabilities
5.5 Working of Azure Batch

6. Azure Data Factory

6.1 Flow Process of Data Factory
6.2 Why Azure Data Factory
6.3 Integration Runtime in Azure Data Factory
6.4 Mapping Data Flows

7. Azure Data Bricks

7.1 What is Azure Databricks?
7.2 Azure Spark-based Analytics Platform
7.3 Apache Spark in Azure Databricks

8. Azure Stream Analytics

8.1 Working of Stream Analytics
8.2 Key capabilities and benefits
8.3 Stream Analytics Windowing Functions: Tumbling window, Hopping Window, Sliding Window, Session Window