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