Introduction to Data Science for recent AI developments

Hello Steemians,

I am very excited about making this post because I am about to take you all on a journey into the exciting world of Data and Artificial Intelligence. As we all know, Data keeps the world going because, in everything you do, you are either giving out data or collecting data. Now, let me dive into what I will be teaching in my blog, but first, let's understand more about Data Science.

Some Mitigating Factors

Data science is one of the best-suited professions to thrive this century. It is digital, programming-oriented, and analytical. Therefore, it comes as no surprise that the demand for data scientists has been surging in the job marketplace. However, supply has been very limited. It is difficult to acquire the skills necessary to be hired as a data scientist.

Universities have been slow to create specialized data science programs. (not to mention that the ones that exist are costly and time-consuming). Most online courses focus on a specific topic and it is difficult to understand how the skills they teach fit into the complete picture

The Solution

Data science is a multidisciplinary field. It encompasses a wide range of topics. Understanding the data science field and the type of analysis carried out would help you navigate the world of Data. Topics that are very important to understand while understudying data science include;

Mathematics
Statistics
Python
Applying advanced statistical techniques in Python
Data Visualization
Machine Learning
Deep Learning

Each of these topics builds on the previous ones. And you risk getting lost along the way if you don’t acquire these skills in the right order. For example, one would struggle to apply machine learning techniques before understanding the underlying mathematics. It can also be overwhelming to study regression analysis in Python before knowing what a regression is.

So, to create the most effective, time-efficient, and structured data science training available online, I have decided to share a post on Data Science for those interested in joining the Journey or just acquiring more knowledge on Data. I believe this is the first training program that solves the biggest challenge to entering the data science field – having all the necessary resources in one place.

Moreover, my focus is to teach topics that flow smoothly and complement each other. My post teaches you everything you need to know to become a data scientist at a fraction of the cost of traditional programs (not to mention the amount of time you will save).

^Source

The Skills

1.Intro to Data and Data Science:

Big data, business intelligence, business analytics, machine learning, and artificial intelligence. We know these buzzwords belong to the field of data science but what do they all mean?

Why learn it?
As a data scientist, you must understand the ins and outs of each of these areas and recognize the appropriate approach to solving a problem. This ‘Intro to Data and Data Science’ will give you a comprehensive look at all these buzzwords and where they fit in the realm of data science.

2.Mathematics

Learning the tools is the first step to doing data science. You must first see the big picture to then examine the parts in detail. We take a detailed look specifically at calculus and linear algebra as they are the subfields data science relies on.

Why learn it?
Calculus and linear algebra are essential for programming in data science. If you want to understand advanced machine learning algorithms, then you need these skills in your arsenal.

3.Statistics

You need to think like a scientist before you can become a scientist. Statistics trains your mind to frame problems as hypotheses and gives you techniques to test these hypotheses, just like a scientist.

Why learn it?
This course doesn’t just give you the tools you need but teaches you how to use them. Statistics trains you to think like a scientist.

4.Python:

Python is a relatively new programming language, and unlike R, it is a general-purpose programming language. You can do anything with it! Web applications, computer games, and data science are among many of its capabilities. That’s why, in a short space of time, it has managed to disrupt many disciplines. Extremely powerful libraries have been developed to enable data manipulation, transformation, and visualization. Where Python shines, however, is when it deals with machine and deep learning.

Why learn it?
When it comes to developing, implementing, and deploying machine learning models through powerful frameworks such as sci-kit-learn, TensorFlow, etc., Python is a must-have programming language.

5.Tableau:

Data scientists don’t just need to deal with data and solve data-driven problems. They also need to convince company executives of the right decisions to make. These executives may not be well versed in data science, so the data scientist must be able to present and visualize the data’s story in a way they will understand. That’s where Tableau comes in – and we will help you become an expert storyteller using the leading visualization software in business intelligence and data science.

Why learn it?
A data scientist relies on business intelligence tools like Tableau to communicate complex results to non-technical decision-makers.

6.Advanced Statistics:

Regressions, clustering, and factor analysis are all disciplines that were invented before machine learning. However, now these statistical methods are all performed through machine learning to provide predictions with unparalleled accuracy. This section will look at these techniques in detail.

Why learn it?
Data science is all about predictive modeling, and you can become an expert in these methods through this ‘advanced statistics’ section.

7.Machine Learning:

The final part of the program, and what every section has been leading up to, is deep learning. Being able to employ machine and deep learning in their work is what often separates a data scientist from a data analyst. This section covers all common machine learning techniques and deep learning methods with TensorFlow.

Why learn it?
Machine learning is everywhere. Companies like Facebook, Google, and Amazon have been using machines that can learn on their own for years. Now is the time for you to control the machines.

Finally, As I share posts on the topics I mentioned above, You will understand the basics of data science and get to understand my informative teachings that will guarantee you more knowledge in the world of Data. No risk for you. The content of my post is going to be excellent, this is a no-brainer for me, as I am certain you will love it.

Let's learn together!!!