Data science is fast becoming one of the most prominent careers globally. Businesses, organization, even governmental bodies are in why is becoming a data scientist so difficult? constant need of someone to help them analyze their data. If perhaps you cherish data science as a career and don't know what steps to take, read on to find out the must-have skills to excel as a data scientist.
Algebra and Calculus
If you are seriously considering data science as a career, you can't overlook algebra and calculus. For starters, they are the basics for numerous data science complexes. And that's not all; they are similarly crucial to the development of most machine learning models. Besides, a deep understanding of algebra and calculus can help your company win big in the market. Indeed, algebra and calculus (particularly linear algebra and multivariate calculus) are useful when projecting performance for a data-defined product. As such, you could advise your employer on when and when not to “spend big”.
Statistics and probability
For most parts, companies require a data scientist to help them make calculated and data-driven decisions. More so, that's the idea behind data science: exploring datasets to make decisions. In that case, you need to master statistics and probability. In reality, statistics reveal the relationship between the variables in your dataset. Probability, on the other hand, enables you to make projections thereof. In the end, they both assist you in guiding your clients to make informed decisions on their businesses.
Programming and programming languages
Ask any data scientist, and they would tell you programming is the soul of data science. Without programming, you can't transform data into actionable insights. In essence, you need to be conversant with a lot of programming languages and coding as a data scientist. If perhaps you don't know what programming language to learn, check our list of programming languages and software for Data science below.
Programming languages and software for Data science include:
- R programming
- SQL coding
- Python coding
Machine Learning (ML) works similarly to statistics and probability. Unlike the two, ML can help you analyze a vast amount of organizational data. Perhaps you've heard that ML is quite should i do a phd or work as a data scientist? i have an offer to work as a data scientist at a really cool company and recently have been offered the chance to do a phd in nlp. the 3–4 year length of the phd really scares me. difficult to learn. If you can identify with R-programming and Python libraries, ML is no different. Moreover, ML has a wide scope: you only need to learn what applies to data science.
To make ML for data science easier for you to work with, here is a roundup of the essential parts:
- K-nearest neighbors
- Regression models – logistic and linear regression
- Random forests
- Methods set
- Naive bayes
- Decision tree
Data visualization and communication
Data visualization allows you to add graphical illustrations to the findings from your data analysis. And that is a good thing. Quoting numbers and various formulas can be quite boring for what don’t people tell you about being a data scientist? your clients.
In total contrast to “numbers and formulas”, graphical illustrations (such as charts, time series, maps, etc.) are fun and engaging. More importantly, they help you to communicate your findings in simple and comprehensible ways to your audience.
To understand data visualization better, check the following tools:
- Microsoft excel
Collating and analyzing data is great. Yet, a data scientist must also learn the rigors of data management. Why is data management important?
Data management refers to all the processes involved in defining, storing, and retrieving your data. Most importantly, it allows you to manipulate your data and perform several tests on it. As a data scientist, clients require you to manage their respective databases. Besides, most of those clients have so many unstructured data.
For those reasons, you need to how can i become a data scientist? understand data management and its tools. Below are common tools you can use for your data management needs:
- IBM DB2
- SQL Server
Let's face it; clients will often bring messy data to you. By messy, we mean incomplete data with missing values or bad formatting. It is particularly common for start-ups with no previous database. To solve the issue of incomplete data, you have to learn data wrangling. So, what is data wrangling?
Data wrangling simply mean cleaning incomplete data. After cleaning your data, data wrangling then gather and group similar data from multiple channels. In essence, data wrangling helps you to maximize time. It ensures that you don't waste time cleaning data manually. That way, you'll have more time to focus on data analysis.
Data-focused problem solving
This is perhaps the most important skill a data scientist should have. It is not enough to master algebra, programming language, and database management. You should also know how to use them creatively to solve your clients' issues.
In all, having the skills listed above will help you launch your data scientist career. However, to excel as a data scientist, you have to be analytical and systematic. In other words, you should know which data science technique is best for any given scenario.
More importantly, you should keep learning and staying up to date with global trends. Technologies are what is the difference between data analyst and scientist? springing up daily, and the same thing applies to data science.
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