Data science is one of the fastest growing and most profitable career paths today. Here, you will learn how to start your career in data science and what skills do you need to become a successful data scientist?
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The Step By Step Guide to Become a Successful Data Scientist?
Data science is one of the fastest-growing careers today. When thinking about what it takes to become a data scientist, it can be hard to uncover the kinds of complex analytical problems that are being solved every day. In fact, a Data Scientist cleans up and interprets large amounts of data in order to discover opportunities or solve problems.
A Data Scientist is someone who extracts and interprets data to strengthen or align with the overall goals of a company. Data Scientists are constantly manipulating data from its raw state to a cleaner, more interpretable presentation.
Data Scientists use data visualization tools to help draw, formulate, and present conclusions or trends that they identify in their daily work.
Data scientists don't usually work alone. Data analysts, data engineers, business intelligence specialists, and architects are the various professionals that a Data Scientist will work with to achieve the goals of their organization.
Companies hire data scientists for a myriad of critical reasons, including developing a greater understanding of customers' pain points, spotting problems with the product or user experience, or analyzing potential growth opportunities.
There are many paths to this career, so those considering what to study to become a data scientist have several options. Data Scientists traditionally come from backgrounds with technical knowledge of programming and statistics. And yes, math is necessary to become a Data Scientist.
Read Here: What is Data Science and What Data Scientists Do?
What Skills Do You Need to Become a Data Scientist?
If you want to become a data scientist, then you must have a passion to understand and analyze user behavior on social media. This passion will help you a lot during your studies. So I encourage you to look for an inspiration that makes you strong while studying.
If you consider becoming a data analyst, a Bachelor's degree in IT, computer science or statistics will give you a strong advantage.
Most Data Scientist positions require at least a bachelor's degree. Degrees in technical fields may be preferred, as are more advanced degrees such as PhDs and Masters, but advanced degrees are generally not strictly necessary.
To become a data scientist, you must acquire the following important skills:
Mathematics, especially algebra, calculus, and statistics:
In the beginning, you just need the basics of algebra and linear algebra as well as differentiation, but for statistics, you need the two branches of statistics: descriptive statistics and deductive statistics, especially probability theory.
The more you delve into mathematics in general and statistics in particular, the better it is for you.
Language Programming especially Python & R:
Nowadays, Python is the most common language among data scientists and AI engineers. As for the R programming language, it contains more than 5000 libraries and is distinguished in data visualization. But Python is more common these days because it is faster than R and both have strong libraries and a strong developer community.
Data visualization:
No one can understand a table with millions of records of data so you need to display big data in a visual image so that you can discover and analyze it. In this course, you will explore different ways of data presentation.
Database Management:
It makes no sense to work with data without having a background on working with data management languages such as SQL and NoSQL.
Machine learning and deep learning:
In this course, you will learn about machine learning and supervised learning versus unsupervised learning as well as deep learning.
Data Mining and Knowledge Discovery:
If you are working in a field related to telecommunications, you must be conversant in the field of communications, and if you work in the field of agriculture, you need to be conversant with agriculture, soil and so on. But don't worry; most fields can gain a good knowledge of them in several months.
How to Build a Career in Data Science: The Ultimate Guide
A data scientist is a professional person making models using data, no one should give you permission to do so. To start with, choose a small project, such as placing the lengths of your house plants in the chart.
Build a model to predict when you arrive to work depending on the weather or a model to estimate the probability that your team will win the tournament.
It doesn't matter what to choose. It doesn't matter if your steps are 100% honest. It doesn't matter if the results are interesting. Just do something.
Don't worry if you don't know how to do all the parts of your chosen project. In fact, it's better to have chosen something you don't know how to do. If you decide to make an app that recommends the best coffee shop based on your location, try making it yourself.
Just diving into the problem will prompt you to learn what you need to finish the project, not afraid of learning a new language or machine learning algorithm or even to write your own algorithm.
Remember, learning is not only done in the traditional sense and classroom. Just learn what you need, and be good enough to build your project and always remember, your project will tell you what you need to learn.
Reinforce your mathematical and programmatic foundations. Data Science requires an advanced understanding of mathematics and a general knowledge of the most popular programming languages.
Learn and become proficient in SQL. SQL is the domain specific language used to extract data from databases. SQL is not as complex as most programming languages, but it is a must learn for anyone who works strictly with large data sets and analytics.
Learn the fundamentals of identifying trends in data. This is crucial to becoming a successful Data Scientist. Remember that many Data Scientists begin their careers as data analysts and continue their programming education as they progress.
Study Machine Learning algorithms, Artificial Intelligence architectures. Data Science is the basis of Machine Learning and, therefore, Data Scientists must demonstrate and reinforce their knowledge of Machine Learning algorithms, Artificial Intelligence architectures and statistics.
Build an application that you are satisfied with. An application you can see for your parents, and after you finish your project, choose another project, and start again.
Is Data Science a Good Career Option?
The demand for Data Scientists is high and the world is generating a massive and growing amount of data every day, so it can be said that Data Science is a fast growing field with high demand and high salaries.
Most Data Science job descriptions require candidates to have programming skills. Data Scientist candidates typically have to show a combination of Python and R programming.
The technical skills required of a Data Scientist usually involve data cleaning and analysis, as well as the implementation of Machine Learning algorithms using a programming language such as Python or R.
Learning Data Science is not easy, but the key is to stay motivated and enjoy what you are doing. If you constantly build projects and share them, you will build your experience and get the Data Scientist job you want.
Read Also:
1. The Importance of Data Science in the Modern World
2. What is Data Engineering and How to Become a Data Engineer?
3. The Difference Between a Data Scientist and a Data Engineer
4. Applications of Data Science in Finance and Business Analytics
5. What is Data Analysis? Data Analytics Trends and Objectives
5. Big Data Types and Characteristics: Applications of Big Data in Real Life