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Technical Guide To Getting Started in Data Science

In this guide, “The Ultimate Guide to Getting Started in Data Science”, we will explain more about How to get started in Data Science.
What does a Data Scientist do?
Data science is the study of how to use advanced analytics and scientific principles to get valuable information from data for business decision-making, strategic planning, and other purposes, such as making better business decisions.
To begin with, Data Scientists need data. It could be finance, healthcare, sports, government, industry, research, entertainment, or software engineering – everyone is producing more and more valuable data. Data is so valuable to businesses because it tells them what people want or need. Besides, it tells exactly who likes their product, when they’re buying it, how they’re buying it, how often they’re buying it.
A data scientist’s job is to turn simple, raw, and unprocessed data into an information gold mine. Essentially, data scientists take an ugly big pile of messy data and turn it into a polished conclusion that everyone can understand. Then they give recommended actions to take based on their conclusions.
Below is step-by-step guide on how to get started in data science.
STEP 1: Learn a Programming Language
First things first, if you want to becoming a data scientist, you must learn a programming language. To help you with choosing a programming language among so many, here are a few, but useful tips.
SUPPORTIVE: You want to make sure there’s a big community for it, which you can turn to for advice, like on stack exchange.
POPULAR: You want lots of pre-written code (libraries) that you can integrate into your own code, like on github. This way, for example, you don’t have to understand how to create a graph from scratch, you can just select the graph you want and feed in your data.
EASY: You also want a language that’s easy to write in, so you don’t make little mistakes that then result in bugs you may spend hours trying to find. This means it’s very easy for you and others to review what you’ve done.
FAST: You want to be able to write programs fast. You want to spend your time analyzing the data not writing code. The faster the programming language lets you create prototypes the better.
POWERFUL: You want to have the option to do long and complex tasks that still run fast and that can be easily integrated into other platforms.
Considering these qualities, the most common programming languages used by data scientists are Python and R. Some other viable ones are JavaScript, C++, Matlab, and SAS. Of course, if you feel more comfortable using another language by all means, do so, at the end of the day whatever you’re fastest in and most comfortable with is what’s best for you!
So now that you have the skills to make your computer crunch data, visualization will set you up to do great analysis and creating comprehensive reports.
Step 2: Make Graphics Your New Best Friends
Visualizations serve two purposes for a data scientist:
 They let you analyze data more easily They make it much easier to communicate what you’ve done with others
 Visualizations play a very important role during your analysis because they let you literally see how your data behaves.
The more you do this, the better you’ll get at being able to differentiate true information (signal) against ones that are just produced through chance (noise).
So, as a data scientist, you’ll be creating the visualizations both to help guide your analysis as well as to visualize results. Once you’ve completed your analysis, if you have to create a report or presentation, you can then pick out the ones that actually say something valuable.
So how can you practice creating and reading these types of graphs? Matplotlib as an amazing library for Python is the answer and a must know for every aspiring data scientist. So I’d highly recommend learning to use that to start making visualizations.
Step 3: Learn How to Analyze Data
A good thing to learn alongside of creating and reading the above types of graphs is how to analyze data. The only way to properly analyze data is to be able to filter, group, drop, aggregate, or manipulate it in other ways. Otherwise, you won’t be able to correctly control and contextualize your analysis, or have the ability to zoom in when answering very specific questions.
Fortunately, Python also has an amazing library for data analysis, called Pandas, that you can just freely download and then use in Python. You can probably start to see why I like Python so much.
As a data scientist you’re going to need data. That data is usually stored in a database. Therefore, you’ll need to learn how to interact with a database.
The most common database type you’ll encounter is an SQL-based database. There are very many different databases that are based on SQL, such as PostgreSQL, Big Query, or MySQL.
SQL databases are also very nice to use because it will speed things up a lot when you start processing, formatting, or even doing part of your analysis in your query, rather than taking out the data and doing all of it afterwards.
Did you follow the steps above? Boom!! You ‘re a Data Scientist.

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