5 Must-Have Skills for Data Science Professionals
“Big data is at the foundation of all the megatrends that are happening.”
– By Chris Lynch, American Writer of Books
Want to become a data scientist? An interdisciplinary field that draws on aspects of math, science, computer science, business and communication, data science allows an organization to make critical business decisions through data exploration, representation, visualization, and presentation, among others. The goal is to turn data into information and turn the information into insights. Most organizations, from healthcare to software and real estate, have realized the importance of data and are now hiring data scientists who can crunch the numbers and come up with valuable insights from the burgeoning information.
Data science as a field is ever-evolving. Therefore, for becoming a data scientist you need to possess the right set of skills, technical and non-technical. In this article, we outline the top skills you need to bag the best data science jobs out there.
5 Must-Have Technical Skills for a Data Scientist
Math, programming and statistical skills are crucial for a data scientist. Data scientists should be able to work with tools such as distributions, statistical tests, and maximum likelihood estimators to develop complex financial or operational models. Data scientists leverage their expertise in mathematics to develop statistical models that can be used to create key business strategies. Understanding and applying logarithmic and exponential relationships are common in real-world data. This is how data scientists can find meaning in data. Some topics of data science that you should be familiar with include:
- Linear Algebra
- Calculus – derivates and gradients
- Probability Distribution
- Random variables
- Hypothesis testing
A data scientist must have the knowledge of programming languages such as SQL, Hadoop, Spark and Scala, Python. Along with C/C++, Java, and Perl, Python is a great programming language for data scientists. Along with this, data scientists must be skilled in SQL (Structured Query Language) which gives you insights when you use it to query a database. The concise commands in SQL save time and reduce the amount of programming you need to perform difficult queries.
Also, when you are faced with a large volume of data that exceeds the memory of your system or the data needs to be sent to different servers, Hadoop plays a crucial role. Hadoop plays a key role in data exploration, data filtration, data sampling, and summarization
Machine Learning and Deep Learning
While data science is the entire process of finding meaning in data, machine learning, a subfield of AI, helps in the process. Machine learning uses data and algorithms to identify patterns and make predictions on the pattern. Data Science is an interdisciplinary field that used a wide range of skills including machine learning, statistics, visualization, etc.
Natural Language Processing
Natural language processing or NLP is the branch of machine learning and data science that deals with text and speech. As the use cases for image classification and NLP are getting more and more frequent in typical enterprise applications, there is a need to acquire at least basic knowledge of deep learning and NLP.
Data visualization is an important part of the data lifecycle. Visualizing and communicating data is important, especially for companies to understand and learn the data and its vulnerability. Using data visualization tools such as Tableau, the data scientist can present the data in a graphical or pictorial format to enable decision-makers to identify areas that need attention, focus on factors that influence consumer behavior and predict sales volumes.
Some of the important components of data visualization include:
- The type of data
- The visualization that is suitable for the data e.g. line graphs, histograms, box graphs, etc.
- Variables to use
- Scale components
- Label components
Most people picture data scientists as lone mathematicians working on complex and huge data sets. However, this is far from eality. Apart from top-notch technical skills, data scientists should also have good communication skills to be able to convey and present very technical information to people. Teamwork, business acumen and the willingness to embrace new technologies are some of the key non-technical skills that a data scientist should try and master to able to apply for the lucrative data science job opportunities.
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Our tailor-made curriculum includes functional analytics, SQL, Statistical Analysis, Text Mining, Regression Modeling, Hypothesis Testing, Predictive Analytics, Machine Learning using R, Python, Deep Learning, Neural Networks, Natural Language Processing, Predictive Modeling, Tableau, Spark, Hadoop, etc. which will help you acquire in-demand skills and start a career as an accomplished data scientist. To enquire, call +91- 9823197779 or email firstname.lastname@example.org