What is Data Science?
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains.
The major topics in the Data Science syllabus are Statistics, Coding, Business Intelligence, Data Structures, Mathematics, Machine Learning, and Algorithms, amongst others.
Most important decisions are made with only partial information and uncertain outcomes. However, the degree of uncertainty for many decisions can be reduced sharply by public access to large data sets and the computational tools required to analyze them effectively. Data-driven decision-making has already transformed a tremendous breadth of industries, including finance, advertising, manufacturing, and real estate. At the same time, a wide range of academic disciplines is evolving rapidly to incorporate large-scale data analysis into their theory and practice.
It’s been said that Data Scientist is the “Sexiest Job of the 21st century”. Why? Because over the past few years, companies have been storing their data. And this being done by each and every company, it has suddenly led to a data explosion. Data has become the most abundant thing today.
But, what will you do with this data? Let’s understand this using an example:
Say, you have a company which makes mobile phones. You released your first product, and it became a massive hit. Every technology has a life, right? So, now it's time to come up with something new. But you don’t know what should be innovated, so as to meet the expectations of the users, who are eagerly waiting for your next release?
Somebody, in your company, comes up with an idea of using the user-generated feedback and picking things that we feel users are expecting in the next release.
Comes in Data Science, you apply various data mining techniques like sentiment analysis etc and get the desired results.
It’s not only this, you can make better decisions, you can reduce your production costs by coming out with efficient ways, and giving your customers what they actually want!
With this, there are countless benefits that Data Science can result in, and hence it has become absolutely necessary for your company to have a Data Science Team. Requirements like these led to “Data Science” as a subject today, and hence we are writing this blog on Data Science Tutorial for you. :)
The most common careers in data science include the following roles.
Microsoft Excel is a commercial spreadsheet application, written and distributed by Microsoft for Microsoft Windows and Mac OS X. At the time of writing this tutorial the Microsoft excel version was 2019 for Microsoft Windows and 2016 for Mac OS X.
Microsoft Excel is a spreadsheet tool capable of performing calculations, analyzing data and integrating information from different programs.
Create and query a SQL database to extract valuable information.
Query and analyze datasets in Python by using Numpy, Pandas, and Pyodbc
Building dynamic and interactive dashboards using Power BI
What you will learn
Machine learning is the study of computer algorithms that improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence
Courses under machine learning with python
-Machine learning fundamentals
-Natural Language Processing (NLP)
-Machine learning scientist
Deep learning: you’ll learn about neural networks, the deep learning model workflows, and how to optimize your models. Use machine learning techniques to solve real-world challenges, such as predicting housing prices, building a neural network to predict handwritten numbers, and identify forged banknotes
Some of the prominent Data Scientist job titles are:
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
Applying advanced statistical techniques in Python
We believe this is the 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).
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 recognise 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.
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.
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.
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 visualisation. 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.
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.
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.
A $1250 data science training program
Active Q-amp;A support
All the knowledge to get hired as a data scientist
A community of data science learners
A certificate of completion
Access to future updates
Solve real-life business cases that will get you the job
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