The most competitive field emerging nowadays, Data Science requires some skill set to be learned so that one can survive in the market. Before getting into the top data science skills to learn in 2020.let’s have an insight of the field itself first.
What is data science?
Data science is simply extracting some valuable knowledge from Big data to answer/solve a particular problem. Data Science is about the clustering of the best tools that can perform the job easily.
Being a Data Scientist, one knows so many things to achieve tasks efficiently. With evolving technology, it’s extremely difficult to update yourself fully but one should know the base requirements for every starting year. This helps an individual to learn the new techniques and tools to survive in the industry.
Data Science skills to learn
Skills listed below are the Top Data Science Skills to learn in 2020. Skills listed are not following any specific sequence but all of them are equally important. Here, I have added these top data science skills into two main categories.
- Core/Technical Skills.
- Advanced/Non-Technical Skills
Core/Technical Skills:
These skills are the basics, or you can say necessities to be hired as a data scientist in any company. If you are not having them then you are simply not eligible for the post. Let’s have an insight into this category and gain some information about the top data science skills.
Basic Maths & statistics
Question might arise why basic maths and statistics?
Basic mathematics include Multivariate Calculus and Linear Algebra. Most of us don’t like these at our schools/college but yes, they are the basics to get eligible for a data scientist post. Along with the combination of Probability &Statistics. Now getting back to your question that why these?
So, Information Science is almost utilizing calculations, capital forms, frameworks to extricate profitable information from a large data set, and use that for decision making, future insights, inferences, etc. Probability and statistics play a vital role as it helps a lot in estimation making for a company. While basic Maths help one to build machine learning models for data science.
Cutting things short, if you want to be a data scientist or planning to switch to data science then don’t forget to learn these
How Probability and Statistics helps data scientist?
- A data science expert can Investigate and get more approximate information from a large data set.
- Highlight the connection and dependence of one variable over the other.
- Foresee future patterns or a float based on past information patterns.
- Expose the anomalies in data
- SQL
Perhaps SQL is the most neglected part of our universities/academies, but it is extremely valuable. 99% of companies use some type of SQL Databases. You might have noticed that everyone advises and teach you about, Mongo DB, or No SQL databases Hadoop, etc. These might look attractive placed in your Bio ( just like colorful accessories added to a stitched shirt) .but remember SQL is the core and if you want to thrive up in the data science field, you must learn the basics of SQL. Not a necessity to know about the stored procedures or admin-level expertise. But basics to pull out the data with filters and optimization of table joints.
Programming skills.
Programming is a major part of Data Science. Without having knowledge /background of coding, you will face difficulties in pursuing data science as a career. With the number of programming languages out there, one can choose the best for their need. For Data Science Python/R is the topmost demanding and in use. Both have their own goods and bad, Data scientist can choose according to the solution they might think of the problem statement in hand. Python is just like a Lingua franca for Data Science. Python for beginners is easy to learn while a programmer with good coding experience can easily work on R-functions. Any one having expertise in both will be considered strong over the one who knows only Python.
So ,leaving this on you that you can choose accordingly by further reading here.
Machine Learning/Deep learning
Machine Learning is considered to be the to data science skill if you are working or planning to work with a company that is data driven. Data driven means the companies who made their future decisions, plans, business insights ,etc. With baseline of data they receive.
For Example : Airlines routes scheduling will be done on the basis of the data the airline receives.
It includes Algorithms like:
K-nearest neighbors. Random Forests, Naive Bayes, Regression Models. PyTorch.
Data Wrangling
Not all the data a company receives or gather is capable of modeling or delivering accurate results.so, data wrangling is about getting understanding of that data, locating the flaws and finding the ways to deal with those imperfections.
Let’s understand it much better by relating it to daily life example. Data Wrangling is just like a woman acquiring all the necessary ingredients/equipment’s to make herself look/feel better before she stands Infront of anyone(e.g. boys’ family for marriage proposal) for further analysis.
Data wrangling is about gathering data, combining the relevant fields and then cleansing it data for further analysis.
With data wrangling,data scientists can give an awfully accurate representation of noteworthy information within the hands of commerce and information examiners in an opportune matter. It helps to center more on the investigation of information, instead of the cleaning portion.
Data visualization:
As the name suggests visualizing data. simply graphical representation of the information obtained from the data for easy analysis and effective communication. It helps to conclude all your exploration you have done through the data.
It is one of the essential skills that brings creativity and science together. That visual representation helps to learn the data and its vulnerabilities.
One of the advantages of visualization is that you don’t have to go through the whole context, you can easily figure out the main and surprising fact of the concerned data. Visualize zones that require consideration or enhancement, components that impact client behavior, understanding which items to put where. Client announcing, worker execution, quarter deals mapping. Plan showcasing procedure focused on client fragments.
Some of the famous tools for visualization of the data are:
- Tableau
- PowerBI
- QlikView
- Google Analytics (For Web),
- MS Excel
- Plotly
- Fusion Charts
- SAS
Histograms, Bar charts, Relationship maps, Heat maps, 3-D Plots, and many more ways can be applied for visualizing the data. For further details visit here. Many of the skills can be listed under this heading but those listed above are very important and top data science skills to learn in 2020 to kick start your career
Advanced/non-technical Skills are also important for Data Science skills to learn
As core skills made you eligible for a position ,Advance/Non-Technical skills will lead you to the powerful candidate who must be considered for a position. Just like a pizza with extra topping to make it more yumilicious.
Let’s have a look at these skills as well:
Communication Skills
“A picture is worth more than 1000 words”.
Being able to deliver your knowledge/findings to your team members/lead is very important. It is observed that new babies in the data science field pay more attention to their coding part as they think that beautiful piece will stand them out. But data-driven companies’ managers are always looking for the best recommendations that they can implement in practice. So, make sure that you have good communication skills and think broadly like a consultant regardless of the level of expertise you have.
Domain expertise:
“An investment in knowledge pays the best interest.”
Benjamin Franklin
If you are new to the company/field learn as much as you can from senior colleagues and team members. Find out the “why/how/what” Questions who is going to use the analysis results, why do they want these analyses? How it will be implemented? does it increase profits? How can I do this faster while maintaining accuracy? What part does the end-user (or manager) really care about?
So, Nothing beats the experience. Don’t pack yourself in a shell of your own mind. Ask others, learn from there experience. Keep on adding a new thing to your knowledge.
Collaboration/Team Work is important in Data Science skills learning:
One of the major issues that people face in any organization is that sometimes they don’t collaborate with their team members, but if you want to be an accomplished data scientist then you have to speed the pace of the result/output so that company growth is good and sustainable. And of course, it requires teamwork you cannot be a hero only. Just like hit movies requires excellent teamwork, you also have to collaborate with your team members(technical/non-technical), stakeholders, etc. It will help the company to emerge bright and high and overcome the organizational issues as well.
Conclusion for data science skills to learn:
So, these are the Top data Science kills to learn in 2020, so you can have a powerful start to your career. Although, these may change according to the trends and problems companies facing or according to their recruitment needs. But despite some of the extraordinary cases, these skills are in demand for data science. You can have a short insight into this by observing this figure that which data science skills to learn on priority.
Therefore,if you want to learn more about data science and the skill set required Click the link below.
If you like this post then also check Free Online IT Courses with Certification where you can find free Data science courses as well.
