5 Best Data Science Tutorials for beginners in localdown time

#datascience #codenewbie #100daysofcode #machinelearning

India is the second-most populous country in the world with a population of 133.92 Crores. With such a large population comes an equally humungous amount of data. Thus, managing, structuring, storing and studying the data is a tough challenge! Well, Data Science is exactly what we need here.

We know the definition of science from our elementary school. Anything related to "cause" and "effect" is called science. In broader terms, the study of nature, behavior, outcomes, and effects of any activity or entity is science. When we take "Data" as the central focus of our study, then Data Science comes into the picture.
When we study Data, we analyze the trends in data. We try to draw patterns in the behavior of the statistics(data). People who are engaged in the domain of Data Science are called Data Scientists.

In a software development agency, Data Scientists study the patterns of data and based on the conclusions made by those patterns they tend to predict the further possible behavior of the entity. It sounds interesting but is equally challenging at the same time.
Considering the above features, it is, undoubtedly, worth learning Data Science. Lucrative compensation and exponential growth are the factors that make Data Science a perfect career option!

Thus, we provide you a highly curated list of Data Science courses that you can consider taking to strengthen your career.

1. Statistics & Applied Data Science - Business Data Analysis

Data Science Statistics : Data Science from Scratch for Beginners : Data Analysis Techniques, Method Course : Analytics

Course rating: 4.3 out of 5.0 ( 238 Ratings total)

Course Contents :

Starting with FAQ related to interview questions in your career .
What is after data analysis ?
Descriptive statistics with collection of important quizzes and examples .
Normal distribution and standard normal in details using Z table .
Sampling distribution with practical simulation apps and answering of important technical questions .
Confidence level and Confidence interval .
what is t distribution ? ( with examples )
What is DEGREE OF FREEDOM ? ( with examples )
One tail and two tail in Confidence level .

You can take Statistics & Applied Data Science - Business Data Analysis Certificate Course on Udemy .

2. Applied Data Science with Python

Learn Applied Data Science with Python from University of Michigan. The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language.

Course rating: 4.5 out of 5.0 ( 13,677 Ratings total)

In this course, you will :

Analyze the connectivity of a social network.
Conduct an inferential statistical analysis.
Discern whether a data visualization is good or bad.
Enhance a data analysis with applied machine learning.
Look at more advanced techniques, such as building ensembles, and practical limitations of predictive models.
Identify the difference between a supervised (classification) and unsupervised (clustering) technique,
Identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis.
Data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis
By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses.

You can take Applied Data Science with Python Certificate Course on Coursera

3. Data Science Statistics for Data Scientists and Business Analysis

Statistics you need in the office: Descriptive & Inferential statistics, Hypothesis testing, Regression analysis

Course rating: 4.5 out of 5.0 ( 9,884 Ratings total)

In this course, you will :

Understand the fundamentals of statistics.
Learn how to work with different types of data.
How to plot different types of data.
Calculate the measures of central tendency, asymmetry, and variability.
Calculate correlation and covariance.
Distinguish and work with different types of distributions.
Estimate confidence intervals.
Perform hypothesis testing.
Make data driven decisions.
Understand the mechanics of regression analysis.
Carry out regression analysis.
Use and understand dummy variables.
Understand the concepts needed for data science even with Python and R!

You can take Data Science Statistics for Data Scientists and Business Analysis Certificate Course on Udemy .

4. Applied Data Science

Learn Applied Data Science from IBM. This is an action-packed specialization is for data science enthusiasts who want to acquire practical skills for real world data problems.

Course rating: 4.6 out of 5.0 ( 11,178 Ratings total)

In this course, you will :

You will learn Python - no prior programming knowledge necessary. You will then learn data visualization and data analysis.
Importing data-sets and cleaning the data. Data frame manipulation and summarizing the data.
Through the guided lectures, labs, and projects you will get hands-on experience tackling interesting data problems.
Solidify your Python and data science skills before diving deeper into big data, AI, and deep learning.
Building machine learning Regression models and Building data pipelines Data Analysis with Python will be delivered through lecture, lab, and assignments.
You will learn how to prepare data for analysis, perform simple statistical analysis, create meaningful data visualizations, predict future trends from data, and more!

Upon completing all courses in the specialization and receiving the Specialization certificate, you will also receive an IBM Badge recognizing you as a Specialist in Applied Data Science. Finally, you will create a project to test your skills.

You can take Applied Data Science Certificate Course on Coursera.

5. Data Science: Statistics and Machine Learning

Learn Data Science: Statistics and Machine Learning from Johns Hopkins University. Build models, make inferences, and deliver interactive data products.

Course rating: 4.2 out of 5.0 ( 3,589 Ratings total)

In this course, you will :

Perform regression analysis, least squares and inference using regression models.
Build and apply prediction functions.
Develop public data products.
Foundations using R specialization.
It covers statistical inference, regression models, machine learning, and the development of data products.
In the Capstone Project, you’ will apply the skills learned by building a data product using real-world data.
At completion, learners will have a portfolio demonstrating their mastery of the material.

You can take Data Science: Statistics and Machine Learning Certificate Course on Coursera

Glad to see, that you have made it till the end. If this article added some value to your learning or if you liked it then like, upvote and share it in your network. In case you want to explore more, visit the free Data Science Courses.

Also, I would love to hear any feedback and review from you. Please tell me what you liked in the comment section below. Happy Learning!✨

Disclosure: This post includes affiliate links; I may receive compensation if you purchase products or services from the different links provided in this article.