Advance Certification in Data Science & Data Engineering

Who can apply for the data science and data engineering certification program?

Individuals with a bachelor’s degree and a keen interest in data science and data engineering.
IT professionals looking for a career transition as data scientists and data engineers.
Professionals aiming to move ahead in their IT careers.
Artificial intelligence and business intelligence professionals.
Freshers who aspire to build their career in the field of data science and data engineering.

What roles can an data science and data engineering professional play?

Senior Data Scientist

Understands the issues and creates models based on the data gathered, and also manages a team of data scientists.

AI Expert

Builds strategies on frameworks and technologies to develop AI solutions and helps the organization prosper.

Machine Learning Expert

With the help of several machine learning tools and technologies, builds statistical models with huge chunks of business data.

Applied Scientist

Designs and builds machine learning models to derive intelligence for the numerous services and products offered by the organization.

Big Data Specialist

Creates and manages pluggable service-based frameworks that are customized in order to import, cleanse, transform, and validate data.

Senior Business Analyst

Extracts data from the respective sources to perform business analysis, and generates reports, dashboards, and metrics to monitor the company’s performance.

Solution Architect

Creates overall technical vision for a solution to a specific business problem, while designing, describing, and managing the solution.

Skills to Master

Python

Data Science

Data Analysis

Data Pipelines

Data Processing

SQL

NumPy

Pandas

SciPy

Git

MLOps

Data Wrangling

Storytelling

Machine Learning

Prediction Algorithms

NLP

PySpark

Model

Data Visualization

Azure Data Engineering

Curriculum

Live Course

Module 1 – Preparatory Session - Python and Linux

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, 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.

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.

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Module 2 – Python with Data Science

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.

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Module 3 – Data Wrangling with SQL

SQL Basics-
- Fundamentals of Structured Query Language
- SQL Tables, Joins, Variables

Advanced SQL-
- SQL Functions, Subqueries, Rules, Views
- Nested Queries, string functions, pattern matching
- Mathematical functions, Date-time functions, etc.

Deep Dive into User Defined Functions
- Types of UDFs, Inline table value, multi-statement table.
- Stored procedures, rank function, SQL ROLLUP, etc.

SQL Optimization and Performance
- Record grouping, searching, sorting, etc.
- Clustered indexes, common table expressions.

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Module 4 – Mathematics & Statistics for Data Science

Basic Mathematics – Linear Algebra, Multivariate Calculus

Descriptive Statistics –
- Measure of central tendency, measure of spread, five points summary, etc.
Probability
- Definition, Random Variable, Probability Distributions and use cases, Bayes theorem, Mathematical Expectation, Markov and Chebyshev Inequality.
Inferential Statistics –
- Correlation, covariance, confidence intervals, hypothesis testing, F-test, Z-test, t-test, ANOVA, chi-square test, etc.

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Module 5 – Machine Learning - Supervised & Unsupervised Learning

Introduction to Machine learning
- Supervised, Unsupervised learning.
- Introduction to scikit-learn, 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.
- 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.
- Evaluation Matrix – r2, adjusted r2, mean squared error, etc.

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Module 6 – Azure Data Engineering

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

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Module 7 – Deep Learning Using TensorFlow

Artificial Intelligence Basics
- Introduction to tensorflow
- Keras API
Neural Networks
- Single Cell (perceptron)
- Multi cell perceptron Topology
- Weights & Biases
- Build a NN from scratch (using numpy)
Deep Learning
- Use cases of DL in industry
- Difference between DS, ML, DL & AI
- Lifecycle of Deep Learning Project

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Module 8 – Natural Language Processing

Text Mining, Cleaning, and Pre-processing
- Various Tokenizers, Tokenization, Frequency Distribution, Stemming, POS Tagging, Lemmatization, Bigrams, Trigrams & Ngrams, Lemmatization, Entity Recognition.
Text classification, NLTK, sentiment analysis, etc
- Overview of Machine Learning, Words, Term Frequency, Countvectorizer, Inverse Document Frequency, Text conversion, Confusion Matrix, Naive Bayes Classifier.
Sentence Structure, Sequence Tagging, Sequence Tasks, and Language Modeling
- Language Modeling, Sequence Tagging, Sequence Tasks, Predicting Sequence of Tags, Syntax Trees, Context-Free Grammars, Chunking, Automatic Paraphrasing of Texts, Chinking.
AI Chatbots and Recommendations Engine
- Using the NLP concepts, build a recommendation engine and an AI chatbot assistant using AI.

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Module 9 – Deploying Machine Learning Models on Cloud

Introduction to MLOps
- MLOps lifecycle
- MLOps pipeline
- MLOps Components, Processes, etc
Deploying Machine Learning Models
- Introduction to Azure Machine Learning
- Deploying Machine Learning Models using Azure

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Module 10 – Data Visualization with Power BI

Power BI Basics
- Introduction to PowerBI, Use cases and BI Tools , Data Warehousing, Power BI components, Power BI Desktop, workflows and reports , Data Extraction with Power BI.
- SaaS Connectors, Working with Azure SQL database, Python and R with Power BI
- Power Query Editor, Advance Editor, Query Dependency Editor, Data Transformations, Shaping and Combining Data ,M Query and Hierarchies in Power BI.
DAX
- Data Modeling and DAX, Time Intelligence Functions, DAX Advanced Features
Data Visualization with Analytics
- Slicers, filters, Drill Down Reports
- Power BI Query, Q & A and Data Insights
- Power BI Settings, Administration and Direct Connectivity
- Embedded Power BI API and Power BI Mobile
- Power BI Advance and Power BI Premium

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Module 11 – Data Science Capstone Projects and Business Case Studies

Data Science Capstone Projects

The Data Science capstone project focuses on establishing a strong hold of analyzing a problem and coming up with solutions based on insights from the data analysis perspective. The capstone project will help you master the following verticals:
Extracting, loading and transforming data into usable format to gather insights.
Data manipulation and handling to pre-process the data.
Feature engineering and scaling the data for various problem statements.
Model selection and model building on various classification, regression problems using supervised/unsupervised machine learning algorithms.
Assessment and monitoring of the model created using the machine learning models.

Business Case Studies

Recommendation Engine – The case study will guide you through various processes and techniques in machine learning to build a recommendation engine that can be used for movie recommendations, restaurant recommendations, book recommendations, etc.
Rating Predictions – This text classification and sentiment analysis case study will guide you towards working with text data and building efficient machine learning models that can predict ratings, sentiments, etc.
Census – Using predictive modeling techniques on the census data, you will be able to create actionable insights for a given population and create machine learning models that will predict or classify various features like total population, user income, etc.
Housing – This real estate case study will guide you towards real world problems, where a culmination of multiple features will guide you towards creating a predictive model to predict housing prices.
Object Detection – A much more advanced yet simple case study that will guide you towards making a machine learning model that can detect objects in real time.
Stock Market Analysis – Using historical stock market data, you will learn about how feature engineering and feature selection can provide you some really helpful and actionable insights for specific stocks.
Banking Problem – A classification problem that predicts consumer behavior based on various features using machine learning models.
AI Chatbot – Using the NLTK python library, you will be able to apply machine learning algorithms and create an AI chatbot.

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Disclaimer

Intellipaat reserves the right to modify, amend or change the structure of module & the curriculum, after due consensus with the university/certification partner.

Program Highlights

9 Months of Live Sessions from Industry Experts

50+ Industry Projects & Case Studies

E&ICT, IIT Guwahati Certification

One-on-one with Industry Mentors

Projects

Projects will be a part of your Certification in Data Science & Data Engineering to consolidate your learning. It will ensure that you have real-world experience in Data Science and Data Engineering.

Face Detection

Use Python 3.5 (64-bit) with OpenCV for face detection. The learners must ensure that the system will have to detect multiple faces in a single image. Students must work with essential libraries such as CV2 and Glob.

Restaurant Revenue Prediction

Work with Ensemble Model for predicting annual restaurant sales using various features like opening data, type of city, and type of restaurant. Work with packages like Caret, Boruta, and dplyr to analyse the dataset and predict the sales.

Work with PySpark & RDD

Work with PySpark which is a Python API for Spark and use the RDD using the Py4J package. As an important part of the project, you will also work with SparkConf which provides the configurations for running a Spark Application.

Build the Book Recommender Application

Work with packages like a recommended lab, dplyr, tidyr, stringr, corrplot, and many others to create your book recommender engine using the ‘user-based collaborative filtering’ model that recommends the books based on past ratings.

Census Project

Work with census income dataset from UCI Machine Learning repository that contains income information for more than 48k individuals. Use data handling techniques to handle missing values and also predict the annual income of people.

Housing Price Prediction

In this project on housing price prediction, work with a house price dataset and predict the sale price for each house with 79 explanatory variables describing every aspect of the houses.

HR Analytics

Learn to work with the HR Analytics dataset and understand how methodologies can help you to re-imagine HR problem statements. Understand the features of the dataset and in the end, evaluate the model by metric identification process.

Joke Rating Prediction

Work with the dataset taken from the famous jester online Joke Recommender system and successfully create a model to predict the ratings for jokes that will be given by the users (the same users who earlier rated another joke)

Build Recommendation Engine

Create a recommending engine by using the SVD algorithm to predict movies on Netflix based on their past ratings. Work with various packages, such as NumPy, pandas, matplotlib and Pyplot, to handle missing values from the dataset.

Legendary of Pokémon

Extract various Pokémon based on a particular parameter and use a classification model to predict the legendary Pokémon. Work with the regression algorithm to predict the attack and defence of a particular Pokémon.

Data Science and Data Engineering FAQs

What can I expect from the advanced certification in data science and data engineering that Intellipaat offers?

This is one of the best data science and data engineering certification courses as it is designed keeping the industry requirement in mind to provide you with the required expertise to handle various aspects of data science and data engineering roles and responsibilities. The career prospects that you will achieve after the completion of the course are innumerable and have highly lucrative opportunities.

Why should I sign up for this data science and data engineering course?

The advanced certification in data science and data engineering is offered by E&ICT, IIT Guwahati and Intellipaat. These instructors aim to make you proficient in the field of data science and engineering and have designed a curated curriculum in the form of online video lectures and projects to help you gain in-depth knowledge of data science and data engineering concepts..

What if I fail to attend one or more lectures?

If you fail to attend any of the live lectures, you will get a copy of the recorded session in the next 12 hours. Moreover, if you have any other queries, you can get in touch with our course advisors or post the questions on our community page.

How will I receive my certification?

On the successful completion of the training program and the fulfillment of all the requirements, including successfully passing the certification exam by Intellipaat, you will be awarded an advanced certification in data science and data engineering by E&ICT, IIT Guwahati.

Why should I enroll with Intellipaat?

Intellipaat is known for its quality training and industry mentorship. Our alumni are placed in reputed organizations globally such as Amazon, Microsoft, Genpact, Sony, Gartner, etc. Our learners also get lifetime access to free upgrades and learning material, which will help them at any point of time in their careers.

By enrolling with Intellipaat’s data science and engineering courses online, you will be able to take advantage of exclusive career guidance benefits, interview preparation, etc.

What is the average compensation of a data scientist and a data engineer?

On average the starting salary of a data scientist is 10 LPA and that of a data engineer is 9 LPA. You can also check our dedicated blog on data science salaries in India based on various job roles.

How many hours do I need to devote to my learning to complete this program effectively?

Learners need to devote at least 8–10 hours per week for effective learning. Our live classes are flexible, and hence, working professionals can easily manage their learning and job together.

What is the duration of this data science and data engineering course?

The duration of this program is nine months, which includes eight months of live sessions, and multiple project hours, and real-life assignments for a month.

What is the refund policy for this program?

Please note that the course fees is non-refundable and we will be at every step with you for your upskilling and professional growth needs.

Do you have the batch deferral policy for this program?

Due to any reason you want to defer the batch or restart the classes in a new batch then you need to send the batch defer request on [email protected] and only 1 time batch defer request is allowed without any additional cost.

Learner can request for batch deferral to any of the cohorts starting in the next 3-6 months from the start date of the initial batch in which the student was originally enrolled for. Batch deferral requests are accepted only once but you should not have completed more than 20% of the program. If you want to defer the batch 2nd time then you need to pay batch defer fees which is equal to 10% of the total course fees paid for the program + Taxes.

Is Intellipaat certification worth it?

Yes, Intellipaat certification is highly recognized in the industry. Our alumni work in more than 10,000 corporations and startups, which is a testament that our programs are industry-aligned and well-recognized. Additionally, the Intellipaat program is in partnership with the National Skill Development Corporation (NSDC), which further validates its credibility. Learners will get an NSDC certificate along with Intellipaat certificate for the programs they enroll in.

Advanced Certification in Data Science and Data Engineering

Ranked #1 Data Science Program by India TV

About the Program

Data Science and Data Engineering Course Key Highlights

Partnering with E&ICT, IIT Guwahati

Career Transition

Who can apply for the data science and data engineering certification program?

What roles can an data science and data engineering professional play?

Senior Data Scientist

AI Expert

Machine Learning Expert

Applied Scientist

Big Data Specialist

Senior Business Analyst

Solution Architect

Curriculum

Module 1 – Preparatory Session - Python and Linux

Module 2 – Python with Data Science

Module 3 – Data Wrangling with SQL

Module 4 – Mathematics & Statistics for Data Science

Module 5 – Machine Learning - Supervised & Unsupervised Learning

Module 6 – Azure Data Engineering

Module 7 – Deep Learning Using TensorFlow

Module 8 – Natural Language Processing

Module 9 – Deploying Machine Learning Models on Cloud

Module 10 – Data Visualization with Power BI

Module 11 – Data Science Capstone Projects and Business Case Studies

Program Highlights

Projects

Face Detection

Restaurant Revenue Prediction

Work with PySpark & RDD

Build the Book Recommender Application

Census Project

Housing Price Prediction

HR Analytics

Joke Rating Prediction

Build Recommendation Engine

Legendary of Pokémon

Reviews

Career Services By Intellipaat

Our Alumni Works At

Admission Details

Data Science and Data Engineering FAQs

What can I expect from the advanced certification in data science and data engineering that Intellipaat offers?

Why should I sign up for this data science and data engineering course?

What if I fail to attend one or more lectures?

How will I receive my certification?

Why should I enroll with Intellipaat?

What is the average compensation of a data scientist and a data engineer?

How many hours do I need to devote to my learning to complete this program effectively?

What is the duration of this data science and data engineering course?

What is the refund policy for this program?

Do you have the batch deferral policy for this program?

Is Intellipaat certification worth it?