Introduction To Data Scientist Skills
With the increase in the amount of unstructured data such as audio, video, GIFs, machine learning training data, or data coming from the social media content, unapproved biological data, the need for data science was acknowledged. Over the past few years, we have come a long way in this field.
Robust and reliable data science infrastructure has been built with the help of the cloud and other distributed computation technologies. Hadoop, Spark, and other contemporary data science tools have made database management efficient and easy to learn.
If you also intend to become a data scientist, then read below to know about the skills required by a data scientist in 2021.
What is Data Science?
Data science is a term that encapsulates a wide range of software paradigms: data analytics, data mining, artificial intelligence, machine learning, deep learning, and various other related disciplines. For building your career as a Data Scientist, you need to acquire the relevant skills for learning it. You can read below about the necessary Data Scientist skills you need in 2021.
Top 10 Data Scientist Skills You Need in 2021
Every enterprise has now realised the need for data-driven decision-making. For becoming a worthy candidate for data scientist role, you need to work on the following skills :
It is the study of the collection, analysis, interpretation, presentation, and organisation of data, as defined by Wikipedia. Hence, it is a clear fact that a data scientist should be familiar with the concept of statistics. For instance, data analysis is based on descriptive statistics and probability theory, at the minimum level.
Graphs and charts are the handiest tools for understanding figures. These concepts assist in making better business decisions by interpreting data.
2. Any programming language – R/ Python
By learning to use a programming language, you get thousands of ways to manipulate data and you may apply numerous algorithms for generating valuable insights from data. Python and R are the most popular languages, exploited by Data Scientists.
The main reason is the high number of packages available for Numeric and Scientific computing. It comes with a few highly advantageous packages such as Scikitlearn in Python and e1071, rpart, etc. in R, for applying Machine Learning Algorithms for data compression.
3. Data Extraction, Transformation, and Loading
Let’s consider a situation in which we have numerous data sources such as MySQL DB, MongoDB, Google Analytics. Your objective is to extract data from these sources, and then transform it such that it can be stored in a required format or structure for the sole objective of running queries and analysing the data set.
Ultimately, you have to load the data in the Data Warehouse, where it can be analysed. Hence, it is said that learners from an ETL (Extract Transform and Load) background become good Data Scientists.
4. Data Wrangling and Data Exploration
Suppose we have sets of data in the warehouse, but those sets are quite inconsistent. Hence, we require to arrange and unify the unstructured and complicated data sets for easy access and analysis, this is known as Data Wrangling.
Exploratory Data Analysis (EDA) is step zero in any data analysis process. In this step, data scientists tend to extract sense from the data they own and then frame the questions they intend to solicit and think about how to frame them, and how they can manipulate the available data sources for achieving the desired results.
This is mainly achieved by giving a broad look at the patterns, trends, outliers, unexpected results, and other important aspects of the database.
5. Machine Learning Algorithms
Machine learning is centred on learning algorithms and using real-time data and experience to predict the future. It refers to the branch that assigns computers the capability of performing without being given any instructions explicitly.
Machine Learning is implemented with the help of Algorithms for processing data and training it for carrying out future predictions without the intervention of human beings. The input for devising the training data for Machine Learning comes from a set of instructions or observations or data. Tech-Savvy companies such as Facebook, Google, Skype widely use Machine Learning.
6. Deep Learning Algorithms
Deep learning is a subset of Machine Learning. Deep Learning can compute an extended range of data resources and demands lower data preprocessing by human beings (e.g. feature labelling). Deep Learning also produces better results than conventional Machine Learning strategies.
Although, it is more expensive than Machine Learning in a few aspects such as execution time, set-up costs, and data quantities. Deep Learning is not a new concept, just like Machine Learning. Artificial neural networks, which are considered to be the prime component of Deep Learning, began to take shape in the early 1940s.
Since then it has achieved major computations. A deep learning network is formed by neural networks, these are interconnected layers of calculators of software origin, these are known as “neurons”.The objective is to replicate an abstracted logic of how the human brain is going to process such kind of information and take reference from the environment and sensory input.
7. SQL databases
SQL is a widely used domain-specific programming language designed for managing and running queries in a relational database management system, which is a type of database for storing and providing access to data points that are coherent to each other.
SQL is used for reading and retrieving data from a database or updating and inserting new data values. Running a SQL query is the primary step in any sequence of data evaluation.
8. Hadoop platform
Hadoop is a set of open-source software tools that allow data scientists to process huge data sets across clusters of computers with the help of basic programming models. It is usually required in case the volume of data is more than the memory of the local system.
For instance, when accumulating a large volume of data from various sources, or when you need to share data across various servers. This system is designed to scale up a single server to clusters of machines for high computation.
9. Big Data Processing Frameworks
A large number of samples are required for training Machine Learning and Deep Learning models. Previously due to limitations in data availability and computational power, building Machine Learning or Deep Learning models were not that feasible.
But, now a dense volume of data is generated at a very high velocity. This data might be structured or unstructured, hence it cannot be processed by conventional data processing systems. These non-homogeneous data sets are known as Big Data.
Therefore, we need reliable frameworks such as Hadoop and Spark for handling Big Data. Currently, major enterprises use Big Data analytics for attaining hidden business insights. It can be concluded as a must-have skill for any Data Scientist.
10. Data Visualisation
Data Visualisation is one of the necessary integral components of data analysis. It is an unavoidable approach to present the data in an easy-to-understand and visually appealing format. Data visualisation is a primary skill that a Data Scientist must possess for communicating efficiently with the end-users. There are numerous tools such as Tableau, Power BI that provide a clean and intuitive interface.
Frequently Asked Questions
Data scientists should be able to analyse large amounts of complex raw and processed information to observe patterns that will assist an organisation and help in driving strategic business decisions. Compared to data analysis, data science is more of a technical skill.
Data scientists are expected to be familiar with quite a lot — machine learning algorithms, computer science fundamentals, statistics, advanced mathematics, data visualisation, communication, and deep learning. These domains include a wide range of languages, frameworks, software methodologies, and algorithms a data scientist must know.
The top three skills required by a data analyst are :
1. A high level of mathematical ability.
2. Hands-on programming languages, such as MySQL, Oracle, and Python.
3. The ability to analyse, model, and interpret data with the help of software solutions.
Machine Learning and Cognitive Algorithm Specialist is one of the top-rated specialisations of data science. With the help of this, beginners and professionals can develop algorithms and Artificial Intelligence (AI) based solutions for providing database management and storage solutions.
No, data science is not a stressful job. If you are genuinely passionate about data handling and manipulation, you are absolutely going to be in your comfort zone and grow in the industry. Data Scientists usually work in a team, in a competitive and rewarding atmosphere, there may be scenarios in which you might have to work alone but that isn’t stressful until you have a passion for data.
After understanding the skills required by a Data Scientist, you can start working on them. Data Science has enabled a lot of tech-based enterprises such as Facebook, Google, Microsoft, Zomato to stand out and provide excellent customer care support and maintain such dense client bases.
If you are thinking of building a career in data science you can learn about the popular Machine learning algorithms, this will help you in dealing with DBMS better and you can work in an enterprise efficiently.
If you want to have a live Data Science experience, devise a few projects of your own. You can check out our courses on Data Science and Machine Learning with Python if you wish to build a few projects on your own under the guidance of our Mentors.
By Vanshika Singolia