Data science has taken the corporate world by the storm. A lot of people want to learn the skills required to get a job as a data scientist. We produce about 2.5 quintillion bytes of data every day. Companies need professionals capable of converting the data into insights and use it for generating profit.
As these organizations are encountering complexities, the demand for data scientists is increasing as they are the only ones who can resolve these problems through efficient data analysis. Over the past few years, data science has become one of the core components of business as it helps them in making well-informed decisions on the basis of statistical data, numbers, and trends.
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Skills You Need to Become a Data Scientist
In order to get a job as a data scientist and become a master in the field, you need to have some essential skills. So, let’s take a look at these must-have skills to become a data scientist:
1. Fundamentals
One of the most important and primary skills for a data science job profile is having a strong understanding of the basic fundamentals and concepts. This includes proficiency in functions, linear algebra, matrices, hash functions, binary tree, database basics, extract transform load, relational algebra, and more.
2. Statistics
Another essential skill that every data scientist should have is statistics. It is a key data science skill that can help you in collecting, organizing, analyzing, and interpreting data.
The important statistics concepts that you have to focus on are descriptive statistics (mean, median, variance, standard deviation, and range), percentiles and outliers, exploratory data analysis, Bayes theorem, probability theory, cumulative distribution function (CDF), random variables, skewness, and other fundamentals.
3. Data visualization
Data visualization can be referred to as representing data graphically. It is a crucial part of the data lifecycle. Having a deep knowledge and hands-on experience in data visualization is a vital skill for a data scientist. You must master visualization tools like Tableau, Google Charts, Data wrapper, and Kibana.
4. Data Ingestion
This is the process of importing, loading, transferring, and processing data to be used or stored later in a database. Data ingestion involves loading data from different sources. It is an important skill that every data scientist must have. Some of the most popular tools of data ingestion that you need to have to become an expert are Apache Sqoop and Apache Flume.
5. Data Munging
Data Munging is the process of cleaning raw data enough so that it can be used as an input for the analytical algorithm. Since it is an integral part of the data life-cycle, it is a must-have skill for a data scientist. You can use Python or R packages for data munging.
6. Data Manipulation
Data manipulation is one of the most essential skills to be a data scientist. It involves modifying and organizing data so that it becomes easier to read. A data scientist will use a programming language as a data manipulation language for adjusting the data by inserting, modifying, and deleting data to map it.
7. Data Integration
Data Integration is the processing of combining data from different sources to provide a unified view. Every data scientist must have hands-on experience working with data integration.
This concept is crucial for organizations as well as it facilitates data analysis for business intelligence. Equipping yourself with data integration knowledge will help you get a job in a data science role in a reputed organization.
8. Programming
Every data scientist’s job requires programming skills. Python is the most common and sought-after programming language used in the field. Apart from Python, another programming language that you must have experience working in is R.
9. Machine Learning (ML)
For companies that are operating on and managing large volumes of data and functioning on a decision-making process that is data-centric, ML is among the most important skills they want in a data scientist. Machine Learning can be considered as a subset of AI that contributes to data modeling. It involves using algorithms such as random forests, regression models, naive Bayes, and k-nearest neighbors.
10. Deep Learning
Deep learning is an advanced type of machine learning. Every organization today deploys a deep learning model as it is capable of solving the limitations of traditional ML approaches.
To be a data scientist, you must have knowledge of neural network fundamentals, libraries like Keras and Tensorflow that are used to create deep learning models, and how recurrent neural networks, convolutional neural networks, and Autoencoders and RBM works.
11. Data Science tools
In order to work in the field of data science, you need to have hands-on experience with some of the most popular data science tools like MS Excel, R, Python, Tableau, Spark, Hadoop, and more.
12. Big Data
If you are in the IT industry, you must know about Big Data. It is a crucial requirement for companies as it helps in improving the decision-making process of the business and get a competitive edge.
13. Problem Solving
Problem-solving is one of the most inherent skills needed to work as a data scientist. You must have an appetite to solve real-world problems. You have to approach a problem productively. To do this, you must know how to calculate the risks associated with business models.
14. Soft skills
Soft skills are just as important as technical skills to become a data scientist. Companies know how important such skills are to work in the field of data science as it helps in understanding the problem at hand and the requirements of the business. Also, with soft skills, data scientists have the capabilities of communicating insights persuasively to the stakeholders.
These were just some of the many skills needed in the job profiles of the data scientist. It is important to remember that data science is constantly evolving. If you want to become and remain an expert in the field, you have to update your data science skills regularly. Join a data science course in Hyderabad today to get started in your journey to become a data scientist.
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