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Data engineering course with industry experience

Learn the principles of data engineering by enrolling in the best data engineering course. Gain valuable experience working on top-tier industry-collaborated projects as well.

 

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300 Hrs

Online Live classes

15+ Projects

Learn from Projects

9 Apr 2022

Next Cohort Starts

Starting 5801/month

Easy No cost EMIs

Land in your dream job with real work experience

Program Features Of

Data engineering course

data-engineering-course-Project-Experince-Certificate.webp

Direct company certification

You get the opportunity to bring your own projects and receive the assistance to complete them. Besides, get project certification from well-known startups.

Design your own courses

Most importantly, you will be able to self-design your courses. In addition, you can get expert level data engineering training by experts.

100% Job Guarantee

To begin, you can assure yourself a job with 100% job guarantee. In addition, you can get free career counseling services and guidance.

Eligibility criteria

This program is recommended for professionals with at least 1 year of work experience. No prior programming experience is required

Land in your dream job with real work experience

Elevate your learning experience enrolling in India's best data science training institute

Get to know in detail about our

Data Engineering program

Basic

₹ 59,000 + GST

Pro

₹ 79,000 + GST

Pro Max

₹ 1,09,000 + GST

Grab real-work experience from top startups

How to get Real Work Experience

complete-required-modules.-of-data-science-and-engineering-course-for-projects.webp

Personalized courses

Tailor made courses to fit your learning requirements
Start your project with ai compnies to Learn data engineering online

Real-time projects

Projects based on different domains to enhance your skill

Project Guidance

Bring your own projects and get proper guidance
data-engineering-course-Project-Experince-Certificate.webp

Direct company certification

Shareable project certification from AI Startups

Get Hired

Work on live projects to get hired at:

theorax
Tcs_logo
Nobroker_logo
crued
Flipkart_logo
wipro_logo
juspay
Vodafone_logo
Nerodynamics_logo
acenture_logo
HSBC_logo
capgemini_logo
Microsoft_logo
rupeek
Zoho_logo
Samsung_logo
Myntra_logo
HCL_logo
Astrozeneca
amazon_logo

Project certification from top AI firms

 Cooperate with companies and work in projects to obtain project experience certificate. In addition, start stepping up for your dream career in the field of data science.

  • Work on real-time projects with AI firms to gain practical experience.
  • Collaborate with well-known companies on projects from inception to end, overcoming obstacles along the way.
  • Get hired Comprehensive training from the best business analytics institute and strengthen your confidence to appear in interviews of multinational companies.

Project certification from top AI firms

Learn From Best

Our Experts Are From

Learn and work

Why Real work Experience?

Shape Your Future

Why get real work experience?

Primarily, most courses only teach students basics. But we emphasize on real work experience. In addition, work experience is helpful in several ways. Some of which are listed below:

Primarily, most courses only teach students basics. But we emphasize on real work experience. In addition, work experience is helpful in several ways. Some of which are listed below:

Syllabus

Data Engineering course curated by leading faculties and industry leaders to provide pratical learning experience with live interactive classes and projects.

Program Highlights
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The Foundations block comprises of two courses where we get our hands dirty with Statistics and Code, head-on. These two courses set our foundations so that we sail through the rest of the journey with minimal hindrance.

Source code Vs bytecode Vs machine code, Compiler Vs Interpreter, C/C++, Java Vs Python.

Different type of code editors in python, Introduction to Anaconda and IDEs

Variable Vs Identifiers, Strings Operators Vs Operand, Procedure oriented Vs Modular programming.

Measures of Central Tendency & dispersion, Inferential statistics and Sampling theory.

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  • Different types of Programming Language.
  • What is Compiler?
  • What is an Interpreter?
  • How a Python Program runs on our system?
  • Features of Python Memory Management in Python,
  • Different Implementations of Python.
  • Conditional Statement,
  • Loop Statement.
  • Linting, formatting, understanding Python code,

  • Command Line Arguments,

  • Python Operators.

  • Working with functions

  • Parameters vs Arguments

  • Namespace vs Scope

  • Function call vs Function referencing.

  • Introduction to Exception Handling

  • Type of Errors

  • Nested try-except block & Default except for block.

  • Introduction to Modular Programming

  • Importing Modules and different import statement

  • Types of Modules.

  • Use of File Handling?

  • Type of Files

  • File Operation

  • What is File Handling?

  • Why do we need File Handling

  • Type of Files

  • File Operation.

  • Intro & use of Regular Expression?

  • Regex module & important methods

  • Regex pattern and it’s interpretation.

  • Intro & use of numpy

  • What is an array?

  • Array Operations using Numpy,

  • Numpy and Scipy

  • Numpy and Pandas.

  • Numpy vs Pandas

  • Exporting Dataframe to CSV and Excel

  • EDA using Pandas

  • Lines & markers

  • Figures, Axes and subplots, Watermark

  • shapes, polygons and arrows Color maps

  • Autocorrelation study.

  • Working with seaborn on titanic dataset,

  • Introduction & installation

  • Controlling figure aesthetics

  • Different plots in seaborn.

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  • Introduction to Probability Principles
  • Random Variables and Probability principles
  • Discrete Probability Distributions - Binomial, Poisson etc
  • Continuous Probability Distributions - Gaussian, Normal, etc
  • Joint and Conditional Probabilities
  • Bayes theorem and its applications
  • Central Limit Theorem and Applications
  • Elements of Descriptive Statistics
  • Measures of Central tendency and Dispersion
  • Inferential Statistics fundamentals
  • Sampling theory and scales of measurement
  • Covariance and correlation
  • Basic Concepts - Formulation of Hypothesis, Making a decision
  • Advanced Concepts - Choice of Test - t test vs z test
  • Evaluation of Test - P value and Critical Value approach
  • Confidence Intervals, Type 1 and 2 errors
  • Chi-squared and F tests
  • Industry Applications - Two sample mean, A/B testing
  •  
  • Ingest data
  • Data cleaning
  • Outlier detection and treatment
  • Missing value imputation
  • Impact of Data Visualisation
  • Univariate Analysis
  • Bivariate Analysis and ANOVA
  • The science of Storytelling
  • Sliding like a management consultant
  •  
  • Capstone Project for Business Analysis
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  • Types of Learning - Supervised, Unsupervised and Reinforcement
  • Statistics vs Machine Learning
  • Types of Analysis - Descriptive, Predictive, and Prescriptive
  • Bias Variance Tradeoff - Overfitting vs Underfitting
  • Correlation vs Causation
  • Simple and Multiple linear regression
  • Linear regression with Polynomial features
  • What is linear in Linear Regression?
  • OLS Estimation and Gradient descent
  • Model Evaluation Metrics for regression problems - MAE, RMSE, MSE, and MAP
  •  
  • Introduction to Classification problems
  • Logistic Regression for Binary problems
  • Maximum Likelihood estimation
  • Data Imbalance and redressal methodology
  • Up sampling, Down sampling and SMOTE
  • One vs Rest (OVR) for multinomial classification
  • Model Evaluation Metrics for classification - Confusion matrix
  • Misclassification error, Precision, Recall, F1 score, and AUC-ROC
  • Choosing the best error metric for a problem
  •  
  • Introduction to Unsupervised Learning
  • Hierarchical and Non-Hierarchical techniques
  • K Means Algorithms - Partition based model for clustering
  • Model Evaluation metrics – Clustering
  •  
  • Introduction to KNNs
  • KNNs as a classifier
  • Non-Parametric algorithms and Lazy learning ideology
  • Applications in Missing value imputes and Balancing datasets
  •  
  • Nonlinear models for classification
  • Intro to decision trees
  • Why are they called Greedy Algorithms?
  • Information Theory - Measures of Impurity
  •  
  • Introduction to Bagging as an Ensemble technique
  • Bootstrap Aggregation and Out of Bag error
  • Random Forests and its Applications in Feature selection
  • How Bagging overcomes the overfitting problem?
  • Scent and Boosting
  • How Boosting overcomes the Bias - Variance Tradeoff
  • Gradient Boosting and Xgboost as regularised boosting
  •  
  • Intro to Time series and its decomposition
  • Autocorrelation and ACF/PACF plots
  • The Random Walk model and Stationarity of Time Series
  • Tests for Stationarity - ADF and Dickey-Fuller test
  • AR, MA, ARIMA, SARIMA models for univariate time series forecasting
  • A regression approach to time series forecasting
  •  
  • Loading data
  • Feature engineering techniques
  • Principal Component Analysis for Dimensionality reduction
  • Linear Discriminant Analysis
  • Feature Selection Techniques - Forward and Backward elimination, RFE
  • Model Tuning and Selection
  • Deploying a Machine Learning Model
  • Serving the model via Rest API
  •  
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  1. Understand the purpose of data modeling 
  2. Identify the strengths and weaknesses of different types of databases and data storage techniques
  3. Create a table in Postgres and Apache Cassandra
  1. Understand when to use a relational database
  2. Understand the difference between OLAP and OLTP databases
  3. Create normalized data tables
  4. Implement denormalized schemas (e.g. STAR, Snowflake)
  1. Understand when to use NoSQL databases and how they differ from relational databases 
  2. Select the appropriate primary key and clustering columns for a given use case
  3. Create a NoSQL database in Apache Cassandran
  4.  
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  1. Understand Data Warehousing architecture
  2. Run an ETL process to denormalize a database (3NF to Star)
  3. Create an OLAP cube from facts and dimensions
  4. Compare columnar vs. row oriented approaches

 

  1. Understand cloud computing
  2. Create an AWS account and understand their services
  3. Set up Amazon S3, IAM, VPC, EC2, RDS PostgreSQL

 

  1. Identify components of the Redshift architecture
  2. Run ETL process to extract data from S3 into Redshift
  3. Set up AWS infrastructure using Infrastructure as Code (IaC)
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  1. Understand the big data ecosystem
  2. Understand when to use Spark and when not to use it
  1. Manipulate data with SparkSQL and Spark Dataframes
  2. Use Spark for ETL purposes

 

  1. Troubleshoot common errors and optimize their code using the Spark WebUI

 

  1. Understand the purpose and evolution of data lakes
  2. Implement data lakes on Amazon S3, EMR, Athena, and Amazon Glue
  3. Use Spark to run ELT processes and analytics on data of diverse sources, structures, and vintages
  4. Understand the components and issues of data lakes
  5.  
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  1. Create data pipelines with Apache Airflow
  2. Set up task Create dependencies
  3. Create data connections using hooks

 

 

  1. Track data lineage
  2. Set up data pipeline schedules
  3. Partition data to optimize pipelines
  4. Write tests to ensure data quality
  5. Backfill data
  1. Build reusable and maintainable pipelines
  2. Build your own Apache Airflow plugins
  3. Implement subDAGs
  4. Set up task boundaries
  5. Monitor data pipelines
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  • Timestamps and Extract.
  • CURRENT DATE & TIME, EXTRACT, AGE.
  • TO_CHAR.
  • Mathematical Functions and Operators.
  • CEIL & FLOOR, POWER, RANDOM, ROUND, SETSEED.
  • Operators and their precedence.
  • String Functions and Operators.
  • SubQuery.
  • Self-Join.
  • ALTER table.
  • CASE.
  • COALESCE.
  • CAST.
  • NULLIF.
  • Check Constraints.
  • Views.
  • Import & Export.
  • What is TABLEAU?
  • Why to use TABLEAU?
  • Installation of TABLEAU.
  • Connecting to data source.
  • Navigating Tableau.
  • Creating Calculated Fields.
  • Adding Colours.
  • Adding Labels and Formatting.
  • Exporting Your Worksheet.
  • Creating dashboard pages.
  • Different charts on TABLEAU (Bar graphs, Line graphs, Scatter graphs, Crosstabs, Histogram, Heatmap, Tree maps, Bullet graphs, etc.)
  • Dashboard Tricks.
  • Hands on exercises.
  • Pre-attentive processing.
  • Length and position.
  • Reference Lines.
  • Parameters.
  • Tooltips.
  • Data over time.
  • Implementation.
  • Advance table calculations.
  • Creating multiple joins in Tableau.
  • Relationships vs Joins.
  • Calculated Fields vs Table calculations.
  • Creating advanced table calculations.
  • Saving a Quick table calculation.
  • Writing your own Table calculations.
  • Adding a second layer moving average.
  • Trendlines for power-insights.
  • Working with Time series.
  • Understanding aggregation and granularity.
  • Filters and Slicers in Power BI.
  • Maps, Scatterplots and BI Reports.
  • Creating a Customer Segmentation.
  • Analyzing the Customer.
  • Segmentation Dashboard.
  • Waterfall, Map Visualization.
  • Pie and Tree Map.
  • Include and Exclude.
  • Categories with no Data

Tools Covered

Industry - partnered capstone projects

Hands-on Projects

Data sets from the industry

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Predict-card-default-skillslash-project

Predict credit default application

Project Objective : Develop a prediction model for existing customers to identify probable credit default for a retail bank
Predict-backrupt-of the company skillslash project

Predict bankruptcy of a company

Project Objective: Model to predict whether a company will go bankrupt or not
Mobile banking project by skillslash

Analyse customer mobile banking

Project Objective: Create clusters of customers on the usage of mobile banking
Predict-card-default-skillslash-project
Predict credit default application

Project Objective : Develop a prediction model for existing customers to identify probable credit default for a retail bank

Predict-backrupt-of the company skillslash project
Predict bankruptcy of a company

Project Objective: Model to predict whether a company will go bankrupt or not

foreign currencey preidction skillslash project
Currency foreign exchange rates

Project Objective: Time series analysis Forecast value of a currency in global market

Mobile banking project by skillslash
Analyse customer mobile banking

Project Objective: Create clusters of customers on the usage of mobile banking

    Edit
    Uber fair prediction skillslash project
    Uber - fare prediction

    Project Objective: Have to analyze the data Identification of COVID-19 surge in cases based on mobility within the country

    Mercedes-reduce0-time to market project skillslash
    Reduce time to market

    Project Objective: Reduce the time for a Mercedes-Benz to reach the market by optimizing the testing

    motion prediction project by skillslash
    Motion Prediction For Vehicles

    Project Objective: Build motion prediction models for self driving vehicles

    Predictive maintenance

    Project Objective: The objective is to predict the failure of the machine in advance

      Edit
      Covid-19 data prediction project
      Google Mobility data Prediction

      Project Objective: Goal is to identify Covid-19 surge in different regions based on mobility within the country

      skillslash-project-newtworking
      Reddit - Vaccine myths on social media

      Project Objective: Sentiment analysis of vaccine on social media

      skillslash-project-ai-heart
      Predict heart failure

      Project Objective: Create a model that could predict heart failure before its occurrence

      Infected or not Infected

      Project Objective: Study the human cell to identify whether it is infected or not infected

        Edit
        Youtube-data prediction project by skillsash
        YouTube trending video analytics

        Project Objective: Analyze daily records of YouTube trending video analytics to generate their comments

        Google Play Store Apps success factors.project by skillslash
        Google Playstore apps success factors

        Project Objective: Predict the factors that contribute to the success of an application on Google play store

        Spotify-song-preidction-project-skillslash
        Spotify – Identify the songs related to

        Project Objective: Work on the dataset to find a geographical connection with popular songs

        Hr-analytics-project-skillslash
        HR Analytics: To find candidates

        Project Objective: Predict the probability of a candidate looking for a new job

          Edit
          tele-customer-skillslash-project
          Telecom customer churn prediction

          Project Objective: Predict the behavior of customers to identify the probability of churning off

          Fraud call prediction skillslash project
          Fraud call prediction model

          Project Objective: Identification of illegal activities like fake profiles, cloning, identity theft for the customers

          telecome price optimization
          Price Optimization by analysing customers

          Project Objective: Price optimization for the telecom services by predicting the LTV, Tarrifs, understanding price elasticity w.r.t factors

          Chatbot developemnt project
          Chatbot design for best in-class support

          Project Objective: Chatbots for operational support and automated self-service

            Edit
            IMDB movie rating prediction
            IMDB – predict the rating of a movie

            Project Objective: Predict the rating and success of movies

            NSE stock prediction skilllsash project
            NSE – Stock price prediction

            Project Objective : Predict the stock prices with an increased level of accuracy

            Vinbigdata project by skillslash
            VinBigData Chest X-ray

            Project Objective: Accurate classification of problems to identify and localize findings on chest radiographs

            Amazon Food Review

            Project Objective: Classify food reviews based on customer feedback. Here you will use NLP to identify the sentiment of customers

              Our story

              Our Mission is to provide world-class education

              Online courses designed for creative and made for everyone. Take course with us. Experience the new era of education

              Our story

              Why pratical learning experience is vital?

              Online courses designed for creative and made for everyone. Take course with us. Experience the new era of education

              Thanks a ton Skillslash Family. As a part of the Full stack course in Data Science i had a great learning experience. I was able to successfully move into a data science role in 7 months , which was amazing.

              Pragyan Prakash

              AI and ML full stack program is too good and helpful for working professionals, I have done BCA so I was well versed in Java, C, basic SQL and C++. At Skillslash I learnt Python, core SQL, R, math - stats, ML and More.

              Tilak Rao

              One of the best platform for working professionals. Although a new startup but training quality is really good. For our batch, instructor is Rahul (and co-founder) and he teaches statistics and ML concepts in depth.

              Gautam

              One of the best course providers is Skillslash, their data science course has helped me become the data scientist I am today. There are tons of differences between studying data science and working as a data scientist.

              Mrinal Sahay

              Skillslash is truly one of the best institutes to study machine learning, I thank my brother for suggesting me this course. The course has amazing perks for working professionals like live classes, faculty of industry professionals

              Sammer Ahmed

                The merits of Skillslash

                Edit
                Certification from top AI startups by skillslash
                1. As part of our Data engineering course, students can work on real-time industrial projects.
                2. From data cleaning through project launch, our students work on real-world projects.
                3. Obtaining a data engineering certification from a reputable company will also help you increase your reputation. Project certification aids in the creation of an attractive project portfolio.
                4. Team decision makers can use this to complete a POC by utilizing. Additionally, get resources for resolving project difficulties.
                Edit
                Build Your own course feature by skillslash
                1. Specialized courses with a concentration on industry training are available. Make use of instructor-led programs as well.
                2. You can create customized learning paths depending on your employment goals and experience using the platform.
                3. Modules can be chosen according to your personal learning method. Additionally, select chapters that are relevant to your profession.
                4. The data engineering course covers a variety of topics and allows you to create your own learning routes.
                Edit
                Bring your own project and get expert guidance by skillslash
                1. Students enrolling in our data engineering course can work on their own projects with our help. In addition, obtain positive outcomes.
                2. Advanced project management skills will be covered in this Data Engineering course.
                3. To better study and use data engineering, students are encouraged to bring their own projects to class.
                4. You will be able to participate in live project training in a comfortable environment.
                Edit
                Certification from top AI startups by skillslash
                1. As part of our Data engineering course, students can work on real-time industrial projects.
                2. From data cleaning through project launch, our students work on real-world projects.
                3. Obtaining a data engineering certification from a reputable company will also help you increase your reputation. Project certification aids in the creation of an attractive project portfolio.
                4. Team decision makers can use this to complete a POC by utilizing. Additionally, get resources for resolving project difficulties.
                Edit
                Build Your own course feature by skillslash
                1. Specialized courses with a concentration on industry training are available. Make use of instructor-led programs as well.
                2. You can create customized learning paths depending on your employment goals and experience using the platform.
                3. Modules can be chosen according to your personal learning method. Additionally, select chapters that are relevant to your profession.
                4. The data engineering course covers a variety of topics and allows you to create your own learning routes.
                Edit
                Bring your own project and get expert guidance by skillslash
                1. Students enrolling in our data engineering course can work on their own projects with our help. In addition, obtain positive outcomes.
                2. Advanced project management skills will be covered in this Data Engineering course.
                3. To better study and use data engineering, students are encouraged to bring their own projects to class.
                4. You will be able to participate in live project training in a comfortable environment.

                How to apply?

                Follow these 3 simple steps to the admission process.

                Step 1: Fill Enquiry From

                Apply for your profile review
                by filling the form

                Step 2: Talk to Expert

                Get your career councelling report from the expert

                Step 3: Get Started

                Join the Data engineering program by enrolling
                Fill Enquiry Form
                Apply for your profile review

                by filling the form

                Talk to Expert
                Get your career councelling report

                from the expert

                Get Started
                Join the Data engineering program

                by enrolling 

                Upcoming Cohort Deadline

                The admission closes once the required number of applicants enroll for the upcoming cohort. Apply early to secure your seats and get started on your professional Data Engineering course.

                15th July 2022

                Finance

                Program Fees & Financing

                The Data engineering course starts from INR 59,000 (Excluding GST). We aim to deliver to you quality education considering the aspect of feasibility.

                Program feasibility

                We are driven by the idea of program affordability. So, we give you several financial options to manage and budget the course expenses. Because we believe in fair reachability and access to all our carefully curated programs. Therefore, you get options such as EMI to pay the course fees

                Program Features

                Job Assistance

                Live Class Subscription

                LMS Subscription

                Job Referrals

                Industry Projects

                Capstone Projects

                Domain Training

                Project Certification from Companies

                Job Guarantee

                Basic

                ₹ 59,000 + GST

                1 Year

                Lifetime

                3+

                7+

                1

                Pro

                ₹ 79,000 + GST

                3 Years

                Lifetime

                5+

                15+

                3

                Pro Max

                ₹ 1,09,000 + GST

                3 Years

                Lifetime

                Unlimited

                15+

                3

                Basic

                ₹59,000 +GST

                1 Year

                Lifetime

                3+

                7+

                1

                Pro

                Price

                ₹79,000 +GST

                Job Assitance

                Live Class Subscription

                3 Year

                LMS Subscription

                Lifetime

                Job Referrals

                5+

                Industry Projects

                15+

                Capstone Projects

                3

                Domain Training

                Project Certification from Companies

                Job Guarantee

                Pro Max

                ₹1,09,000 + GST

                3 Year

                Lifetime

                Unlimited

                15+

                3

                Batch Details

                Program Cohorts

                The Data engineering course next cohorts 2022
                The Data engineering course next cohorts 2022

                15 July 2022

                26 June 2022

                08:00 – 10:00 AM

                09:00 – 12:00 AM

                Weekday (Mon – Fri)

                Weekend (Sat-Sun)

                Got Question regarding next cohort date?

                Frequently asked questions

                Read the FAQ’s below to know more about our data engineering training, fees and project details.

                The data engineering course lasts for six months. The amount of time you spend learning will vary depending on your schedule (weekdays/weekends). Also, you will be able to study a variety of topics under different courses. Especially, starting with the principles of programming, such as python and cloud basics, and progressing to advanced Excel, DBMS - SQL and NoSQL, and concepts like ETL and python with object-oriented programming.

                We believe that any eager data science aspirant can make a successful data science career move. No, we don't have any severe prerequisites for eligibility. But, we do, however, examine candidates' ability for the data engineering course. Particularly, on the basis of programming abilities. The data engineering course has been designed with experts in mind. Besides, a candidate enrolling must have at least one year of professional experience in any field.

                The data engineering training has a course fee starts from INR 59,000. In addition, you can also avail a scholarship program under our data engineering training. You can get up to a 10% scholarship for the Fresh Graduates program if you take an aptitude exam. So, if a candidate has a score of more than 65 percent on the aptitude exam, he or she will be eligible for a 10 percent reduction on the course fees.

                Above all, we provide live online classes. Additionally, all of our students have access to video recordings of those classes. Besides, we provide you with unrestricted access to these recorded sessions so that you can refer to them whenever you need theoretical assistance in your data engineering, AI or ML career. As a result, if you miss any of the live classes, you won't be disappointed. That said, we do, however, strongly encourage you to attend all live classes.

                 

                The catch is that you will not receive any form of certification or academic degree if you attend the data engineering course. Instead, we provide a project management certificate that is recognized all over the world. Besides, direct certifications are also available from the company with which you completed your industrial project.

                The data engineering course offers 15+ real-world projects from which you can choose a couple for case study learning based on the demands of your domain. Aside from that, during the following modules, you will have the opportunity to work on three industry projects (capstone projects) with a variety of MNCs and startups.

                For queries, feedback & assistance

                Get Free Career Counselling

                (7AM -12 AM)

                For Working Professionals & Freshers

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                For Working Professionals