For the past 5 years, data scientists have been one of the most desired and hottest jobs in the world. As soon as companies started realizing the importance of data in their businesses, the demand started growing in every sector. Today data science has become the core that supports businesses for analytics, mining or extraction, NLP, ML, AI, etc.
The decisions that they (businesses) take are now solely dependent on the proposed data (by data scientists or their relevant hierarchy) and they’re helping them (companies) to take helpful decisions. This has triggered the huge jump of such professionals over the past few years and is still dominating the industry. Due to this, the pay scale is pretty decent for data scientists and that’s one of the major reasons why people are paving their way toward this domain.
But the path to becoming a successful data scientist is not easy as it may sound, it requires a set of skills that companies do look for. To ace your career in this field, you’re required to master a handful of tools and languages along with statistical computations (besides strong communications and interpersonal skills). So, to help you with that let’s discuss the top 7 Skills Required to Become a Successful Data Scientist.
1. It all Starts With the Basics – Programming Language + Database
Without the knowledge of programming language, it’s all meaningless because then you would not be able to perform any task to generate insight. That’s why being a data science professional would require you to have knowledge of certain programming languages to manipulate the data and apply sets of algorithms as and when required. However, there are certain major languages that are used by data scientists and most importantly the recruiter would also want you to possess these languages. Following is the list of programming languages:
Besides this, there are a few important databases that are required to store data in a structured way and ensure how and when data should be called when required. Some of the most popular databases used by data scientists are:
Among this list, only Python and R programming are majorly used by data scientists for generating adequate outcomes that are desired by most companies irrespective of their domain. They do offer frameworks and packages that are helpful to gather numeric and statistical data.
2. Mathematics
This is something that can’t be ignored if you’re choosing your career in this field. To perform tasks and execute for the desired output, it is expected to have a strong command of statistics and mathematics. Below is the list of topics that you need to cover to get fluency while working as a data scientist.
These are the topics that are required for you to cover to make your base strong while working in the data science field. All the major algorithms are going to flow with this process so ensure that you’re learning them thoroughly so that you can implement them in any real-life scenarios.
3. Data Analysis & Visualization
Do you know that every day more than 2.5 quintillion bytes are being generated which is a huge figure in itself and that’s what creates the urge for businesses to translate those data into a useful format? Being a data scientist would require you to work on data visualization to display the pictorial forms of charts and graphs that can be easy to understand. There are hefty of tools that are being used and some of the popular ones are:
- Tableau: This is one of the most effective tools used for data analysis and visualization by data scientists across different industries. It enables users to extract the desired output without an actual single line of code and has been widely accepted by companies such as Nike, Amazon, Coca-Cola, etc.
- Power BI: Among all, this is one of the most famous tools that is being used by organizations today. Introduced in 2014, is a business analytical tool to prepare data sets and analyze them on different scales. The best part about this is that it’s absolutely free of cost and open to use (unlike others) and that’s what makes it more demanding among data scientists.
- Qlikview: Another elegant tool and the biggest competitor of the tableau is QlikView. Being one of the widest used tools for data visualization is best for generating the desired output when it comes to data visualization and is also easy to deploy in your project.
- D3.js: To support data visualization in web browsers, d3.js was introduced first in 2011 (a javascript library) that supports HTML/CSS and SVG. Besides this, it also enables data scientists to map their data with its (SVG) attributes easily.
4. Web Scraping
Technically, whatever data that do exist over the internet can be scraped when required. This method is used by companies to extract useful data such as text, images, videos, and other valuable information to enhance productivity. Details could be customer reviews, surveys, polls, etc. Companies of every level (from small to large) are actively practicing this method (under a limitation as per law) and using certain tools and software for this method can simplify this process by handling data on large scale. When it’s all about data everywhere, web scraping has been in huge demand among data scientists.
If you don’t know about it, let’s read What is Web Scraping and How to Use It?
Some of the most popular tools used for data scraping are:
- BeautifulSoup: It’s a python library that is used by data science experts to extract and parse data from the websites directly to local or database. To get started with this library, you are required to install it using the terminal refer to this article: BeautifulSoup Installation
- Scrapy: Commonly used for data mining, and gathering useful content from any particular website as and when required. Besides the fact, that it was introduced back in 2008 for the purpose of web scraping but today, it is widely used for data extraction using APIs (such as AWS)
- Pandas: A python library that can be used to manipulate data for data extraction and can be exported in the form of Excel or CSV.
To read more about Web Scraping, refer to this article: “Web Scraping Tutorial with Python”
5. ML with AI & DL with NLP
Machine Learning with Artificial Intelligence
Having a deep understanding of machine learning and artificial intelligence is a must to have to implement tools and techniques in different logic, decision trees, etc. Having these skill sets will enable any data scientist to work and solve complex problems specifically that are designed for predictions or for deciding future goals. Those who possess these skills will surely stand out as proficient professionals. With the help of machine learning and AI concepts, an individual can work on different algorithms and data-driven models, and simultaneously can work on handling large data sets such as cleaning data by removing redundancies. But for being proficient would require having a specific aligned course for data science such as Complete Data Science Program – Live Course that is well tailored to prepare any individual right from scratch.
There are two major techniques that need to be taken care of, those are:
- Supervised machine learning: Method of predicting a future outcome for any unforeseen data that learn from labeled training data.
- Unsupervised machine learning: A type of machine learning that is designed to train using an unlabeled dataset and act as a stand-alone i.e. without any supervision.
Deep Learning with Natural Language Processing
The primary motive for deep learning being successful with NLP is its accuracy in delivery. One must understand that deep learning is an art that requires a set of specific tools to show its caliber. For example, the “Automatic Text Translation” tool, this tool enables users to translate any given line of sentence that is provided to perform this action. So, in other words, it requires computers to understand human languages by enabling such algorithms. Being a proficient data scientist, you are required to have a strong command of certain programming languages such as Python and Java, and also it becomes easy for computers to understand the natural language.
To read in-depth about this, refer to this article: ML | Natural Language Processing using Deep Learning
6. Big Data
As we’ve discussed above, a hefty amount of data is being generated every day and that’s where big data is being primarily used to capture, store, extract, process and analyze useful information from different data sets.
Those who have already worked handling big data may understand that handling such an amount of data is not really feasible due to multiple constraints (both physical and computational) and tackling such challenges requires special tools and algorithms to achieve such goals. Some of them are:
- KNIME: A data preparation platform used to create specific sets of data by aligning both design and workflows
- RapidMiner: An automated tool that is designed with visual workflow and is being used for data mining.
- Integrate.io: It’s a platform used to ingrate, process, and prepare different data sets for analytics on the cloud.
- Hadoop: An open-source platform used to store and process large sets of data that can extend from gigabytes to petabytes.
- Spark: One of the best and highly popular tools that are used to quickly handle large datasets and are widely used by telecom, game companies, etc. To read more about Apache spark, refer to this article: Overview of Apache Spark
*Note: The amount of data that we create everyday, “let’s say 2.5 quintillion”, so these data are collected from various sources like Mobile devices, software, geolocations, other multimedia devices and so on and that’s why it requires data scientists to handle data at such large scale by using different tools and technologies.
7. Problem-Solving Skill
The base of establishing your career as a data science professional will require you to have the ability to handle complexity. One must ensure to have the capability to identify and develop both creative and effective solutions as and when required. You might face challenges in finding out ways to develop any solution that possibly needs to have clarity in concepts of data science by breaking down the problems into multiple parts to align them in a structured way.
Being a professional in one of the highest urges of demand fields would definitely require you to act stand-apart and think out of the box.
Add On: Model Deployment
Last but not least required skill is having the knowledge of model deployment that enables putting machine learning into production. Thus, this enables users to use prediction models for their projects by which they can make future business decisions (based on extracted data). DevOps can be the best example for deployment which aims to integrate the software development team and software operations team. However, this is considered one of the most challenging skill sets, and even companies don’t even mention such skills in their JDs but having knowledge of model deployment will definitely be a plus point and will make your stand apart from the rest.
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