Skills Needed to Become a Data Scientist – Learn, Grasp, Implement!
Do you know – We perform 40,000 search queries every second (on Google alone), which makes it 3.5 searches per day and 1.2 trillion searches per year. – Forbes
Dealing with such huge amount of data is not your cup of tea, until or unless you are a data scientist. And for becoming a data scientist you need to possess the right set of skills. There are some technical and non-technical skills needed to become a data scientist.
So, without wasting any more time, start upgrading your skills with DataFlair. And for that, you need to explore the complete blog😉.
A data scientist is better statistician than any software engineer and better engineer as compared to any statistician. A data scientist is termed to be the “sexiest job of the 21st century.
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Data Scientist needs to have both technical and non-technical skills to perform their job in an effective manner.
Technical Skills needed to become a Data Scientist
Below are some of the technical skills needed to become a data scientist. I have divided this part into three sub-categories – Statistical skills, Mathematical skills, programming skills for a data scientist.
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1. Statistical & Probability Skills
Statistical Thinking is the most important aspect of Data Science. You cannot be a Data Scientist without having the required statistical knowledge. Data Science is basically the rebranding of statistics.
While traditionally, statisticians with formal degrees in Statistics could study Data Science, it is now possible for people without any formal degree to study Data Science. There are various books that offer statistical insights about Data Science and teach practical aspects of it.
We will also introduce you to some of the concepts of statistics that you must have in your skillset for your data science journey. Generally, Statistics is divided into two categories –
Descriptive Statistics deals with summarizing and describing the data. It quantitatively summarizes large features of data through visualizations and outlines the sample from a larger population of data values.
Some of the measurements in descriptive statistics are normal distribution, variability, kurtosis & skewness, central tendency etc.
Inferential Statistics is about inferring or concluding from the data. It is about drawing conclusions from a smaller sample and implying the drawn conclusions over a larger group. There are various statistical methods in inferential statistics that you must know about.
Some of these methods are – Central Limit Theorem, Hypothesis Testing, ANOVA, Quantitative Data Analysis. With these essential techniques, you will be able to attain the required skills for statistics.
Another skill that is needed to become a data scientist is Probability. Concepts of probability is the backbone of data science and one must be skilled at it in order to carry out complex machine learning operations. It is used in inferential statistics and designing of Bayesian Networks.
You also need to be accustomed to conditional probability as it is intensively used in machine learning algorithms like Naive Bayes. It is a key skill that helps you to ascertain the uncertainty and chances of events. Using this, you can make decisions which can assist your company.
Therefore, a combination of statistics and probability is an important skill that you should possess as a Data Scientist.
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2. Mathematical Skills
Mathematics is another important part of Data Science. If you want to become a proficient Data Scientist, then you must be proficient in these topics – Linear Algebra, Calculus, Discrete Math and Optimization Theory. We will discuss the various important aspects of these topics in detail:
The first and foremost skill for acquiring a mathematical aptitude for Data Science is Linear Algebra. Linear Algebra powers everything that runs on Machine Learning. It is used in the artistic rendering of your photographs, recommendation systems and facial recognition.
The knowledge of Linear Algebra is a must to have skills. There are various topics in Linear Algebra like matrices, tensors, matrix factorization, eigenvalues, etc. Linear Algebra is also used in conjunction with statistics where it is used for DImensionality Reduction in Principle Component Analysis.
The knowledge of calculus is another important skill that a Data Scientist must possess. Calculus is used extensively in Data Science, especially for tasks that require machine learning.
Some of the important topics of Calculus are – Maxima & Minima, Functions of single & multiple variables, partial derivatives, differential equations, etc.
Calculus is used in calculating loss function which is the most important concept in optimizing models. The concept of partial derivates is also used in backpropagation for neural networks.
Discrete Math is essentially the math for programming. The topics of Discrete Math include – Boolean Algebra, Set Theory, Relations & Functions, number theory, recursion, graph theory, etc.
Unlike topics like calculus that makes use of continuous values, discrete math is the study of values that are distinct and separate. Discrete Math is also useful when dealing with databases, for example, the set theory can be applied to the inner-joins and outer-joins of the table.
Optimization is extremely important for Data Science. Having knowledge of optimization and being skilled in it makes you know how to use data effectively. It teaches you how to find the most optimal solution in a complex multi-dimensional space.
There are three parts of optimization – Variables, Constraints and Objective Function. In a simpler analogy, variables are the parameters that can be tuned. Constraints bind the variables and create a boundary for them.
The objective function is the goal to which our algorithm drives the solution. Optimization is a must-have skill for Data Scientists as it allows you to make the best out of data and develop better models.
Learn the important topics of Maths and Statistics for Data Science in just a few minutes
3. Programming Skills
Programming is the skill that differentiates a data scientist from a traditional statistician. A data scientist, along with the knowledge of statistics & math must also know how to put his knowledge in practice. Basically, programming allows you to implement your statistical thinking in a practical setting.
Without programming, you cannot put your knowledge to practice. Therefore, you must be skilled at programming to solve problems of Data Science. Some of the essential programming languages and tools that you must know for Data Science are –
Python is the easiest programming language that you can learn for Data Science. It offers a simple learning curve, Python is highly versatile, meaning that you can use it for different tasks and operations.
It enjoys a wide range of libraries and functions that you can implement in your code to develop robust models. Python supports all the functions of data science. Some of the libraries that you must know for Data Science are –
Pandas is a Python library used for data wrangling. Data Science will require to clean and preprocess data. In order to do so, he/she need to have pandas in your skillset.
Data Visualization is the most important skill for data science. It is the form of visual communication that companies require. Maplotlib allows you to visualize data through scatter plots, line plots, image plots, histograms, 3D plotting, pie-charts, log plots, etc.
Numpy allows a data scientist to compute complex and multi-dimensional matrices. Therefore, it is a must know skill for aspiring data scientists.
Classifying data and developing prediction models is the most important part of the data science process.
Companies seek candidates who not only possess the knowledge of machine learning but also the practical skills to implement them on datasets. One such library that allows you to practice machine learning is Scikit-learn.
Tensorflow is an advanced library that is used for processing deep learning algorithms. It is widely used for developing models for image recognition, speech recognition, art generation, etc.
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R is a statistical programming tool that is used for solving core-data science problems. While R provides a steep learning curve, the knowledge of this language can help you stand apart from the crowd. For data science companies, R is a must-have skill for prospective data science candidates.
R offers various packages that can appeal to a wide variety of statistical needs. With over 10,000 packages in its CRAN repository, R has emerged as the most favorable tool for solving complex data analysis problems in various fields like astronomy, biostatistics, genomics, finance, etc.
Some of the important packages of R that you must be skilled at in order to become data scientist are –
ggplot2 is an important data visualization package for R. As mentioned above, companies require data visualization as an important means of communication. Therefore, you must have the required skills to express data visually. R allows you to perform this using ggplot2.
This package provides you with the skills to manipulate data. It allows you to organize data in rows and columns and in particular, ‘dataframes’. Dplyr allows fast-paced performance for complex data analysis tasks.
purrr is an essential data wrangling tool provided by R. It provides you with easy to use functions that can map and aggregate data. In order to apply wrangling operations in R, you must be well versed with this package.
It is a visualization package that allows you to develop interactive web applications that depict aesthetic graphs and plots. You can develop individual applications or you can embed the visual plots in your R code.
This skill will give you an upper edge over other candidates who possess conventional data science skills.
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Tableau is a visualization software that allows you to develop and share interactive visualizations. While Tableau is closed-source proprietary software, beginners and data science aspirants can acquire this skill through Tableau Public.
It allows you to connect spreadsheets and databases with Tableau and create interactive dashboards. Using Tableau Public, you can share your visualizations on a public platform.
Various types of visualizations in Tableau are Bar Charts, Line Charts, Pie Charts, Maps plots, scatter plots, Gantt Charts, Heatmaps, etc.
Database Query Languages
There are two types of Database Query Languages one must know. You should be skilled at Relational (SQL) as well as non-Relational Database Query Languages (noSQL). A relational database is a collection of structured data in rows and columns.
This form of data is usually generated by mobile devices, IoT devices, and services that can be easily managed. You must be skilled at SQL which is designed for querying database models. A
long with SQL, you must also be knowledgeable about NoSQL which allows you to deal with an unstructured form of data. It is to be noticed that the skill of NoSQL is most important as companies usually deal with unstructured data in the form of customer reviews, emails, etc.
Some of the SQL languages are MySQL, PL/SQL etc. whereas NoSQL languages are MongoDB, Cassandra, Redis, etc.
Big Data Technologies
The knowledge of Big Data is highly treasured by the industries. In order to achieve an established position of a Data Scientist, you must have the required skills of Big Data. It is one of those technologies that have become a buzzword recently.
If you have the knowledge of Big Data, you will be able to catch the attention of a large number of recruiters.
It is an added skill that will increase the overall value of your data science skillset and make you highly industrially oriented. Some of the trending big data technologies that you can add to your skillset are –
Apache Hadoop is an open source big data platform that is written in Java. It is interoperable in multiple programming languages like Java, Python, C++, Perl, Ruby etc.
Hadoop is the most widely and commonly known Big Data tool and if you are proficient in it, you will be able to handle large volumes of data.
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Spark allows real-time streaming management of data. Having Spark in your skillset will boost your value tremendously. As Spark is an improvement over Hadoop and more and more companies are using it to build their big data architectures, it can prove to be a very beneficial technology.
Apache Flink is a recent Big Data Technology. While many industries are still new to this concept, it is gradually developing a strong foothold owing to its event-driven applications. It is a robust platform for streaming analytics, making it an ideal tool for companies that deal with real-time data.
Some of the event-driven phenomena that are making use of Flink are fraud detection, business monitoring, anomaly detection etc. Having Flink in your resume will boost your chances of grasping that data science position.
Non-Technical Skills needed to become a Data Scientist
Here are some of the non-technical skills required to become a Data Scientist –
1. Data Inquisitiveness
Inquisitiveness or curiosity to learn more is the key towards acquiring mastery of any quantitative field. Since Data Science is highly quantitative in nature, it requires someone with expertise and knowledge. Therefore, one must be armed with the curiosity to learn more and experiment with data.
Since Data Science is constantly evolving, you must stay ahead of the curve by updating yourself with articles, blogs, new updates in programming languages, tools, etc. This requires a high magnitude of intellectual curiosity for learning new concepts and implementing them.
2. Business Expertise
Data Science revolves around the business domain and therefore requires the data scientist to have knowledge of the business requirements. The main goal of a data scientist is to translate business problems into data science solutions through the implementation of analytical skills.
There are several different businesses that make use of Data Science in their own way. Therefore, a data scientist must have a degree of adaptability in order to showcase business expertise in every scenario possible.
Have you checked the implementation of Data Science in Businesses?
3. Communication Skills
Communication Skills are utmost important for Data Scientists. It is one of the non-technical skills that you cannot ignore. Some of the important areas in Data Science where communication skills are important as Data Visualization and Storytelling.
Teamwork is another important quality of Data Scientists. Data Scientists work on projects that require the combined efforts of several team members.
As a Data Scientist, you have to work with several members of the company like business analysts for understanding customer requirements, marketing department and software team for product development. Therefore, teamwork is essentially important.
You need to work with your team members to understand the business problems and use data to solve those problems using analytical solutions. Similarly, you have to work together to meet deadlines and deliver the product in due time.
So, these were some skills needed to become a data scientist. Data Science comprises of Statistics & Probability, Mathematics, and Programming, that’s why you should have the right attitude to understand their various underlying concepts.
After all, Data Science is a lucrative career that draws a lot of people and therefore, requires a lot of investment when it comes to skills.
TIP – If you are mentioning Hadoop and Spark skills in your resume then there is more chance that you get selected. The reason behind this is, companies are looking for data science professionals with Big Data knowledge.
You can upgrade these skills through learning and registering at DataFlair for highly recommended tutorials by professionals – Big Data Hadoop and Spark Online Course
Hope you have understood all the skills needed to become a data scientist. Give your feedback through comments. It will help us to serve you better.