Foundation Course in Science and Technology

The Problem

Data science is one of the best-suited professions to thrive this century. It is digital, programming-oriented, and analytical. Therefore, it is no surprise that the demand for data scientists has been surging in the job marketplace.     

However, supply has been minimal. Acquiring the skills necessary to be hired as a data scientist isn't easy.    

And how can you do that?  

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 isn't easy to understand how their teaching skills fit into the picture.  

The Solution   

Data science is a multidisciplinary field. It encompasses a wide range of topics.   

  • Understanding of the data science field and the type of analysis carried out  
  • 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 correct order. For example, one would struggle to apply Machine Learning techniques before understanding the underlying Mathematics. Or, it can 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, we made The Data Science Course 2023.   

We 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, our focus is to teach topics that flow smoothly and complement each other. The course 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).   

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 candidate 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 data science.  

   2. Mathematics 

Learning the tools is the first step to doing data science. You must first see the big picture to examine the parts in detail.   

We 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. You need these skills in your arsenal to understand advanced machine-learning algorithms.

   3. Statistics 

It would be best if you thought 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 its capabilities. That’s why, in a short time, it has disrupted many disciplines. Compelling libraries have been developed for data manipulation, transformation, and visualization. However, Python shines when it deals with machine learning and deep learning.

Why learn it?   

When developing, implementing, and deploying machine learning models through powerful frameworks such as scikit-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 were all disciplines invented before machine learning. However, these statistical methods are now 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; 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 independently for years. Now is the time for you to control the devices.   

Who this course is for:

·        You should take this course if you want to become a Data Scientist or if you want to learn about the field

·        This course is for you if you want a great career

·        The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills