Comprehensive training in data science: math, statistics, Python, advanced statistics in Python, deep and machine learning
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ENDS IN:
Problem Data Scientist is one of the most suitable professions to thrive in this century.
It is digital, programming-oriented and analytical. Therefore, it is not surprising that the demand for data scientists has increased in the job market. However, supply has been very limited.
It is difficult to acquire the necessary skills to be hired as a data scientist. 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 very expensive and time consuming).
Most online courses focus on a specific topic and it's hard to understand how the skill they teach fits into the bigger picture. The Solution Data science is a multidisciplinary field. It covers a wide range of topics. Understanding of the field of data science and the type of analysis performed Mathematics Statistics Python Application of 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.
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Or it can be overwhelming to study regression analysis in Python before knowing what a regression is. Therefore, in an effort to create the most effective, time-efficient and structured data science training available online, we created Data Science Course 202. We believe this is the first training program that solves the biggest problem. challenge of entering the field of data science with all the necessary resources in one place.
Additionally, our goal is to teach topics that flow seamlessly 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'll save).
The abilities
Introduction to data and data science
Big data, business intelligence, business analytics, machine learning and artificial intelligence. We know that these buzzwords belong to the field of data science, but what do they all mean? why learn it
As a data scientist candidate, you need to understand the ins and outs of each of these areas and recognize the proper approach to solving a problem. This Introduction to Data and Data Science will give you a comprehensive look at all of these buzzwords and where they fit into the realm of data science. .
Maths
Learning the tools is the first step to doing data science. You need to see the big picture first, then examine the parts in detail. We take a closer look specifically at calculus and linear algebra, as they are the subfields on which data science is based.
Why learn it? Calculus and linear algebra are essential to data science programming. If you want to understand advanced machine learning algorithms, then you need these skills in your arsenal. .
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, like a scientist. Why learn it? This course not only gives you the tools you need, but also teaches you how to use them.
Statistics trains you to think like a scientist.
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 is why, in a short time, he has managed to disrupt many disciplines.
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Extremely powerful libraries have been developed to enable data manipulation, transformation, and visualization. Where Python really shines, though, is when it comes to machine and deep learning. Why learn it? When it comes to developing, deploying, and deploying machine learning models through powerful frameworks like scikit-learn, TensorFlow, etc. Python is an essential programming language.
Artwork
TableauData 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 needs to be able to present and visualize the data story in a way that they understand.
That's where Tableau comes in and we'll help you become an expert storyteller using the leading business intelligence and data science visualization software. Why learn it? A data scientist relies on business intelligence tools like Tableau to communicate complex results to non-technical decision makers.
Advanced statistics
Regressions, clustering, and factor analysis are all disciplines that were invented before machine learning. Now, however, all of these statistical methods are done through machine learning to provide predictions with unparalleled accuracy. This section will discuss these techniques in detail.
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