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[Code: EARLYBF30] Eduonix Data Science and Engineering E-Degree

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Data Science and Engineering E-Degree on Eduonix Learning Solutions. Build your career in Data Science using languages and tools like Python, Excel, R, Tableau, MySQL, PySpark and multiple hands-on projects.

[Coupon Code: EARLYBF30] Eduonix Data Science and Engineering E-Degree

Why This E-Degree

With this E-Degree, you will gain in-depth learning of all the comprehensive concepts of Data Science & Engineering in a step-by-step manner.

With this E-Degree, you’ll get:

  • 9 Comprehensive modules with a well-defined structure
  • Step-by-step interactive learning from scratch
  • Content curated from world-class instructors
  • Practical projects covering real-world issues
  • Hands-on training from industry experts
  • Exams & quizzes to measure your progress
  • Certificate upon completion of the E-Degree
  • Support for interview preparation
  • Expert-verified responses to ensure quality learning
  • E-books, guide books & code snippets
  • Lifetime access & update with no limits
  • 100% online & self-paced curation

What you’ll learn

With this E-Degree, you will explore the world of data science with structured modules having well-defined interactive content from world-class industry leaders. It will help you understand the science behind Data Science in a step-by-step manner.

Module 1: Data Scientist Tool Set – The Starter Tools

  • Learn how to import data into Excel from an Access database and CSV file and work with Data using VLOOKUP and Pivot tables in Excel.
  • Write complex SQL query to fetch data and solve the business problems with the help of SQL.
  • Complete understanding of foundational python.

Module 2: Advanced Tools for Data Scientists

  • Understand the most important concepts relating to the R programming language.
  • Understand key concepts like Regression and Decision Trees in R programming.

Module 3: Basic Data Visualization

  • Plot categorical, quantitative and mix of both type of data visualisations in Python.
  • Create data driven impressive insights using various kinds of plots with Titles, Labels, Legends etc and carry out quick exploratory data analysis using visuals in python using subplots, pair-plots etc.

Module 4: Statistics & Mathematics for Data Science

  • Get grasp on the most common statistical concepts applied in the field of data science and work through their application using Python coding and Google Colab notebooks.
  • Work with Confidence Intervals and Hypothesis Testing, Regression, Predictions, Classification Modelling as well as NLP.

Module 5: Machine Learning with Python

  • Understand the capabilities of machine learning (ML), and the knowledge to formulate your business problem to solve it effectively.
  • Build an army of powerful Machine Learning models and know how to combine them to solve any problem.
  • Master your Machine Learning fundamentals with algorithms and clustering using K-means.

Module 6: Business Intelligence

  • Ramp up your SQL skills by looking at advanced features of MySQL such as unions, views, triggers, stored procedures and more.

Module 7: Basic Data Analytics

  • Learn how to design databases to ensure data integrity. As a bonus, get a small introduction to NoSQL systems with MongoDB.
  • Work on Data Cleaning, Correlation Analysis, Time Series Analysis, Model Selection, Regression models and Decomposition in Business Intelligence in Data Science.

Module 8: Data Engineering Basics

  • Learn about the most common topics within the field of big data, you’ll have the vocabulary and skills needed to pursue applications in the field of Big Data.
  • Features and value of core Hadoop stack components including the MapReduce programming model.
  • Learn concepts of Data Mining and get introduced to the core techniques using practical exercises in Python, R and Rapid Miner.
  • Understand Data reduction, Data clustering, Anomaly detection, Association analysis, Regression analysis, Sequence mining and Text mining (Sentiment Analysis).

Module 9: Professional Data Engineering

  • Learn basic and advanced data visualization, dashboard and story development and integration of Tableau with Python and R.
  • Achieve scalable, high-throughput and fault-tolerant processing of data streams using PySpark.

Curriculum for This Data Science and Engineering E-Degree Course

This is an extensive designed program that will cater to all your Data Science needs. This complete E-Degree program includes 9 up-to-date modules, comprehensive courses, various labs and projects, quizzes and so much more to ensure complete learning in the most efficient way.

  1. Module 1: Data Scientist Tool Set – The Starter Tools
  2. Module 2: Advanced Tools for Data Scientists
  3. Module 3: Basic Data Visualization
  4. Module 4: Statistics & Mathematics for Data Science
  5. Module 5: Machine Learning with Python
  6. Module 6: Business Intelligence
  7. Module 7: Basic Data Analytics
  8. Module 8: Data Engineering Basics
  9. Module 9: Professional Data Engineering

Why learn Data Science

The demand for data scientists has grown so unprecedented that Hiring Lab accepted that data science postings have rocketed to 256% – more than triple since 2013. But sadly, McKinsey said there was over 50% gap in the supply of data scientists versus demand. It was mainly due to the major skill gap which somehow is choking this positive technological change.

Let’s have a look on some exceptional statistics:

  • The US leads the data science market, requiring 190,000 data scientists by next year
  • The data science platform market size is expected to grow from $37.9 billion in 2019 to $ 140.9 billion by 2024
  • Every second 60,000 search queries are performed on Google and 1.2 trillion searches per year
  • 1 billion pieces of content are shared via Facebook’s Open Graph every day.
  • The U.S Bureau of statistics predicted an estimated 11.5 million new jobs in this field by 2026
  • Data Analytics Market is expected to grow at a CAGR of 30.08% from 2020 to 2023, which would equate to $77.6 billion
  • A 10% increase in data accessibility will result in more than $65 million additional net income for the typical Fortune 1000 company
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