For Beginners: Basic Learning Resources to Begin the Data Science Journey by Parin M.

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I have completed my Data Science Training from Fingertips Co.: Data Intelligent Solutions.

• Company’s Website: https://www.fingertips.co.in/ 
• Social Media Presence: Instagram, Youtube, Facebook, LinkedIn
• Address: 201, Olive Arcade, Chimanlal Girdharlal Rd, opp. Samudra Complex, Navrangpura, Ahmedabad, Gujarat 380009 ( Google Maps: https://g.page/fingertips-ahmedabad )

Further, This article includes the information I have prepared about the web resources I could find according to my potential that will be helpful for you to start your exploration in the field of Data Analysis and Data Science as a whole.

Before giving the resources for Data Science and Machine learning, which are the subset of Artificial Intelligence as a whole, I will need to brief you about Data Science, so you KNOW well about what you can expect from these studies. 

From ‘Data36’ website

(Detailed Basic introduction to ‘What is Data Science’: https://data36.com/what-is-data-science )

• What is data science? 

A broad definition would be something like this:

You have a large amount of data and you’re trying to extract something smart and useful from it.

It says that if you want to be a data scientist, you have to be good at three things:

1. Statistics
2. Coding
3. Business

What is data analysis?

Usually, you will use your data for 3 major things in your data science projects:

1. Data analysis (e.g. reporting, optimization, etc.)
2. Predictive analytics (predicting the future)
3. To build a data-based product (eg. a self-teaching chatbot, a recommendation system, etc.)

The word data analysis refers to the most conventional way of using your data. You run analyses to understand what happened in the past and where you are now. Let’s say you have this chart outlining the first 16 months of your product sales:

Why are they so important?

1. Coding

Coding is inevitable because that’s the tool you need to work with your data. It’s like the piano for the pianist, the brush for the painter, or the pen for the poet. If you want to make your ideas come true, you have to know and use your tools as a professional. (The most popular data science languages are: SQL, Python, and bash.

2. Statistics

Statistics is the actual science of your data science projects. After all, data is about numbers. And when you work with numbers, you should be confident with mathematical and statistical concepts, right?

I know that many people are afraid of (or even more: they hate) statistics. But statistics is not boring nor extremely difficult. It’s only that it has bad marketing. To become a data scientist, you have to be familiar with statistical concepts like statistical averages, statistical biases, correlation analysis, probability theory, functions — machine learning algorithms, of course — and so on…

3. Business knowledge

The third topic is business knowledge. This is a soft factor. For example, let’s say that you are working for a bank as a Data Analyst. You can be the best coder and the best statistician, but if you don’t understand the business concept behind interest rates or how mortgages work, you will never be able to deliver a meaningful data analysis. I wrote more about the business aspect of data science in this article: Data Science for Business.

So data science is an intersection of three things: statistics, coding, and business.

Note 1: Of course, to be successful in the long-term in data science, you have to build other soft skills like presentation skills, project management skills, or people skills.

Now, Let's jump to the Learning Resources:

These are the resources that I could find that I believe will be helpful for you. Below are the websites that provide a wide variety of knowledge for the Data Science field, so you can find the ones that suit you:

FREE CODE CAMP

NO PREMIUM. NO PAID COURSES.

Completely free site. Non-profit organization and a good standard to learn technical stuff. Data science. Artificial intelligence. Machine learning.

WEBSITE: Learn to Code for Free – Coding Courses for Busy People 

YouTube channel: freeCodeCamp.org

DATA36 by Tomi Mester

www.data36.com

Website articles. Lectures on YouTube. Email subscription. PDF guides, cheat sheets, etc.

His TEDx presentation

Why we should value data in our decisions (Ténykonfliktus) | Mester Tamás | TEDxYouth@Budapest 

Data36 Newsletter: Data36 Inner Circle (Newsletter and Other Free Data Science Resources) 

Contact :

Follow him on Twitter: Tomi Mester (@data36_com)

Follow him on Youtube: Tomi Mester - Data36

365 DATA SCIENCE YouTube channel.

 Mainly, a YouTube channel. Lessons, updates of the field of data science jobs. 

 365 Data Science

“Towards data science” website by Medium 

• https://towardsdatascience.com/

A Beginner's Guide to the Data Science Pipeline | by Randy Lao ??

Statistics

Statistics made easy! Learn about the t-test, the chi square test, the p value and more: https://www.youtube.com/watch?v=I10q6fjPxJ0

Statistics - A Full University Course on Data Science Basics 

Microsoft Excel:

• In Excel, a few of the functions you will need to learn for good data analysis expertise are as below. Search for them online and on Video tutorials on youtube as well.
• Sumif
• Averageif
• Countif
• Basic Arithmetic Operator
• Data Validation
• Various Filters In Spreadsheet
• Text To Column
• Transpose
• Index And Match
• Logical & Multiple If
• V- Lookup, And H Look Up
• Making Pivot Table
• Making Charts And Graphs

 AND... FUNCTIONS LIKE : 

• Mean
• Mode
• Median
• Max
• Min
• Range
• Std Dev
• Count
• Sum
• Sub
• Div
• Random
• Length Of String
• Sort
• Remove Duplicates
• Filter
• Conditional Formatting

1. Microsoft Excel Tutorial for Beginners | Excel Training | Excel Formulas and Functions | Edureka https://www.youtube.com/watch?v=RdTozKPY_OQ 
2. https://www.coursera.org/learn/excel-data-analysis 

Tools and skills That I learned were: 

• Machine learning
• MySQL
• Python Programming
• R-Studio for Statistical Computing
• Advance Excel
• Microsoft Power BI
• Tableau

If you are interested in data analysis, the biggest tool you have to master is advanced Excel. Then comes the representation of data which is called data visualization, For that, the most widely used software is Microsoft Power Bi and Tableau.

For a further advanced level of data science's field of machine learning, you will be required to learn python programming. The further advanced level is the software for statistics is called R studio. 

For SQL : 

• SQL Tutorial - Full Database Course for Beginners 
• SQL For Data Science Tutorial | Learn SQL Database For Data Science | Edureka
• https://www.datacamp.com/projects 
• https://www.coursera.org/learn/sql-for-data-science 

Code Basics is a great source for Python - Pandas - Machine Learning

• for Python and Machine Learning, the best and understandable for NON-ITs I have found is Code basics. It should give you the best head start for your journey to explore Data Science by Python :

PLAYLISTS: 

• codebasics 
• Machine Learning Tutorial Python | Machine Learning For Beginners 
• and explore his channel more for python and ML
•  Mathematics, statistics for data science and machine learning

This Medium.com Article explains the complex Machine Learning Algorithms in Layperson terms: 

I believe that It would be great to have a conceptual understanding before Diving Into the world of Machine learning models… something which I should have done before I started. 

https://towardsdatascience.com/machine-learning-algorithms-in-laymans-terms-part-1-d0368d769a7b 

• I found a library that runs 30 algorithms in ML modelling at once. Easy Life. ?? May be useful in projects/works to determine which algorithm will provide the highest accuracy. https://towardsdatascience.com/how-to-run-30-machine-learning-models-with-2-lines-of-code-d0f94a537e52

• Python for Finance: Python Programming for Finance
• https://towardsdatascience.com/exploratory-data-analysis-visualization-and-prediction-model-in-python-241b954e1731

R studio (R software for statistical computing analysis)

Download: Download the RStudio IDE 

Basic tutorial: R Programming Tutorial - Learn the Basics of Statistical Computing

https://www.youtube.com/watch?v=iROHLA_TXQM

• Now, for the data visualization part, it will interest you, as It is a bridge between loads of data and the making of sense to it. A bridge between a data analyst and his clients 

these links below should give you a head start to explore more about data visualization 

Tableau:

• Tableau for Data Science and Data Visualization - Crash Course Tutorial 
• Edureka: Tableau Full Course - Learn Tableau in 6 Hours | Tableau Training for Beginners | Edureka 

Power Bi:

• Power BI Tutorial From Beginner to Pro ? Desktop to Dashboard in 60 Minutes ? 
• Data analysis in Power BI - Learn | By Microsoft Learn
• Power BI Learning Resources by Ms. Pragati Jain

Power BI Learning Resources Pragati Jain 

• A brief and useful introduction for Google Data Studio. It requires no setup. 

The biggest pros are that processing power will be high, no matter how big the data is, due to it being an online platform. 

Also, it is real-time updating (charts, data sources, etc.) while sharing and collaborating with others. 

A 10-minute crash course on Google Data Studio 

Just sharing a tip; 

Might come in handy someday for Data Visualization, just in case there is no local machine (laptop/Desktop) available and you need to prepare Visualization all online. 

• Also, a tutorial video:  

Introduction to Google Data Studio

PS: 

• I encourage you to watch this YouTube Original Documentary about Artificial Intelligence, hosted by 'The Iron Man' Robert Downey Jr.

 " AGE OF A.I. ": The Age of AI 

It consists of 8 episodes. Every episode explains the usage of A.I. in different fields, improving efficiency, saving Humans and Earth.

We will see the Use of A.I. for different purposes like the Prediction of the future based on Learning Algorithms, Revolutions in the Food Industry, Healthcare, Space, Biology, Farming, and more. It will motivate you. Every episode is worth watching.

• Read this medium Article on the common misconception: The Actual Difference Between Statistics and Machine Learning

Wishing you the best for your beginning of exploration in the field of Data Science.

• Parin M. 

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