Before we move on to discussing the necessary knowledge, let's explain why you want to grow a Data Science specialist at all.
We exist in a data-driven society. What performs organizations important is the size, uniqueness, and nature of the data they hold stored throughout their existence. Each new byte of data allows corporations to earn more. Presently the number of the world's population is at its peak, as great as the number of Internet users, which means that the volume of data consumed and disclosed by users is extremely high. And this is not the limit.
Companies now require skilled characters who may operate effectively with large datasets and support develop results.
According to Andrew Chamberlain of Glassdoor Data Science, he leads the list of best jobs. The list is based on employee satisfaction, salary levels and demand for specialists. Data scientists must be needed in all areas of business - from medical services and non-profit organizations to retail. Learn more from data science service provider.
1. Learn Python
We meant learning the basics of programming in principle. But time is a limited resource, so if you do not consider yourself a programmer yet, then you should begin with Python. Why? It combines the demand for specialists, ease of learning, and versatility.
2. Review the basics of statistics and math
Statistics are commonly viewed as one of the mainstays of data science. However because this is a vast area of research, it can seem overwhelming. There is a dedicated podcast that explains the math required to analyze data.
In general, for a start, it is worth understanding the theory of possibility, mathematical reasoning, the regression form, and the essence of exchange.
A data scientist is a person who knows statistics better than any technician and knows how to write code properly than any statistician.
3. Get familiar with SQL
All companies from Facebook to the New York Times use databases, most often using SQL. You demand to study SQL to quickly add, change, or retrieve data from these databases.
One of the most useful sources to study is the SQL Zoo. Another option is our articles. You can also use the available SQL Cheat Sheets as it will be hard to remember all the functions at once.
4. Understand how algorithms work
Algorithms for computer programs are like recipes for cooks (beginners). This is a series of instructions, following which the program correctly does what it wants from it. There are many algorithms.
Algorithms may be divided within three main types: linear, branching, and repetition.
And this is a listing of items to learn:
- Extended regression
- Logistic regression
- Simple Bayesian Classifier
- K-nearest acquaintances approach
- Maintenance vector tool
- Choice tree
- Accidental cover
5. Improve your presentation skills
You should learn how to properly visualize the effects of your work. To show events professionally, you require understanding how to manage various data visualization books in Python. Plus, you'll get an account if you learn to operate with technologies like Tableau. It is more comfortable and more suitable for people to get information using visual effects, rather than searching for enormous quantities of data.
6. Follow the area of professionals
Table of Reddits for Those Who Want to Grow a Data Scientist Many singles have a harder time reaching their goals than those who interact with like-minded personalities. As a piece of society, you will not simply jump into the data science field faster, but you will also be the first to know about new trends that are important to work. It would be strange to lag after competing colleagues, developing in the same way as they do. At the very least, it is worth checking regularly for project updates from the realm on GitHub.