Data Science Masterclass

Data scientist 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, the supply has been very limited. It is difficult to acquire the skills necessary to be hired as a data scientist.  

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Data science is a multidisciplinary field. Modern data-science is an amalgamation of the following skills: 

 

  • Understanding of the data science field and the type of analysis carried out  

  • Mathematics  & Statistics

  • Python  

  • SQL 

  • Big Data & Spark 

  • Data Visualization  

  • Machine Learning  

  • Deep Learning 

 

1. Programming Language 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 really 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 scikit-learn, TensorFlow, etc, Python is a must have programming language.  

 2. 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 the realm of data science.  

 

   3. 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.

 

 4. 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.


 

 

 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 but be able to present and visualise the data’s story in a way they will understand. That’s where Tableau comes in – and we will help you become an expert story teller using the leading visualisation 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 modelling and you can become an expert in these methods through this ‘advance 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.