Free Data Scientist Courses – learn data science

Gain the critical skills needed to become a data scientist for free , rated one of the best jobs in America and in demand globally.

Today we will review how to learn data science  and where to find free data scientist courses  every week we will add more courses  or skills you can learn for free to become data scientist let’s start

  • There are approximately 215,000 open job positions in Data Science (Source:Indeed.com)
  • There are currently about 31,000 openings for Statistician positions in the US (LinkedIn), offering an average salary of $77,000 (Glassdoor)
  • In the field of Business intelligence, there are around 14,000 openings in the US (LinkedIn) at a $88,000 base salary
  • Careers include data scientist, data engineer, data analyst, statistician, data manager, data architect, business analyst
  • Data Scientist is rated the best job in America for 2017, with a median base salary of $110,000. (Source: Glassdoor)

 

1: first courses University of California, San Diego

The course will be at edx.org online

Course content :

MicroMasters program encompasses two sides of data science learning: the mathematical and the applied.

Mathematical courses cover probability, statistics, and machine learning. The applied courses cover the use of specific toolkit and languages such as Python, Numpy, Matplotlib, pandas and Scipy, the Jupyter notebook environment and Apache Spark to delve into real world data.

You will learn how to collect, clean and analyse big data using popular open source software will allow you to perform large-scale data analysis and present your findings in a convincing, visual way. When combined with expertise in a particular type of business, it will make you a highly desirable employee

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2: Data Science Essentials

This course is part of the Microsoft Professional Program Certificate in Data Science and Microsoft Professional Program in Artificial Intelligence.

Course content :

you will learn key concepts in data acquisition, preparation, exploration, and visualization taught alongside practical application oriented examples such as how to build a cloud data science solution using Microsoft Azure Machine Learning platform, or with R, and Python on Azure stack.

Explore the data science process
Probability and statistics in data science
Data exploration and visualization
Data ingestion, cleansing, and transformation
Introduction to machine learning
The hands-on elements of this course leverage a combination of R, Python, and Microsoft Azure Machine Learning

 

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3: Foundations of Data Science

 

help you become a data scientist by teaching you how to analyze a diverse array of real data sets including economic data, geographic data and social networks. Typically, the information will be incomplete and there will be some uncertainty involved. You will then study inference, which will help you quantify uncertainty and measure the accuracy of your estimates. Finally, you will put all of your knowledge together and learn about prediction using machine learning.

Data Science is one of the fastest growing job areas in the US, drawing demand from a variety of industries including technology, manufacturing, retail, government, and finance.
An enormous variety of organizations need to augment their capacity to make effective data-driven decisions.
The average salary for an Entry Level Data Scientist is $118,748. (Source: Glassdoor)

 

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4: Programming for Data Science University of Adelaide

apply fundamental programming concepts, computational thinking and data analysis techniques to solve real-world data science problems

You will learn algorithm design as well as fundamental programming concepts such as data selection, iteration and functional decomposition, data abstraction and organisation. In addition to this you will learn how to perform simple data visualisations using ProcessingJS and embed your learning using problem-based assignments

 

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5: Business Analytics Columbia University

learners with a series of courses that emphasizes the use of statistical analysis, computing tools, and mathematical models to predict the outcomes of various business decisions, and identify the best implementation

The use of business analytics has grown exponentially in all areas, including healthcare, government, retail, e-commerce, media, manufacturing, and the service industry.
The result is an increased need for employees with an analytical approach to management who can utilize data, understand statistical and quantitative models, and are able to make better data-driven business decisions.
Career prospects include business analyst, business intelligence analyst, business development manager, market research analyst, marketing intelligence analyst, digital marketing manager, chief operating officer, strategist, and chief marketing officer.
We also expect successful learners to gravitate toward start-up organizations.

Course content :

Apply methods, tools, and software for acquiring, managing/storing, and accessing structured and unstructured data
Prepare data for statistical analysis, perform basic exploratory and descriptive analysis, and apply statistical techniques to analyze data
Apply descriptive, predictive and prescriptive analytics to business modeling and decision-making
Demonstrate orally, and in writing, the ability to explain complex analytical models and results

 

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