The Top Skills for a Career in Datascience in 2021

Is Datascinece a good career choice?

Datascience is exploding in popularity due to how it's tethered to the future of technology, supply-demand for high paying jobs and being on the bleeding edge of corporate culture, startups and innovation!

Students from South and East Asia especially can fast track lucrative technology careers with data science even as tech startups are exploding in those areas with increased foreign funding. Think carefully. Would you consider becoming a Data Scientist? According to Coursera:

A data scientist might do the following tasks on a day-to-day basis:

• Find patterns and trends in datasets to uncover insights

• Create algorithms and data models to forecast outcomes

• Use machine learning techniques to improve quality of data or product offerings

• Communicate recommendations to other teams and senior staff

• Deploy data tools such as Python, R, SAS, or SQL in data analysis

• Stay on top of innovations in the data science field

In a data-based world of algorithms, data science encompasses many roles since data scientists help organizations to make the best out of their business data.

In many countries there's still a shortage of expert data scientists that are familiar with the latest tools and technologies. As fields such as machine learning, AI, data analytics, cloud computing and related industries get moving, the labor shortage of skilled professionals will continue.

Some Data Science Tasks Are Being Automated with RPA

As some tasks of data scientists become automated, it's important for programming students and data science enthusiasts to focus on learning hard skills that should continue to be in demand well into the 2020s and 2030s. As such I wanted to make an easy list of the top skills for knowledge workers in this exciting area of the labor market for tech jobs.

Shortage of Data Scientists Continues in Labor Pool

The reality is for most organizations in most places there's still a significant shortage of data scientists.

So the idea here is to acquire skills that are more difficult for RPA and other automation technologies to automate at organizations. It's also important to specialize in skills where business penetration is high but increasing faster as the majority of businesses are adopting the trend, like Cloud computing and Artificial Intelligence.

AI Jobs Will Grow Significantly in the 2020s

In India, according to LinkedIn, AI is one of the fastest growing jobs. LinkedIn notes, Artificial Intelligence roles play an important role in India’s emerging jobs landscape, as machine learning unlocks innovation and opportunities. Roles in the sector range from helping machines automate processes to teaching them to perceive the environment and make autonomous decisions. This technology is being developed across a range of sectors, from healthcare to cybersecurity.

The top skills they cite are Deep Learning, Machine Learning, Artificial Intelligence (AI), Natural Language Processing (NLP), TensorFlow.

With such a young cohort of Millennials and GenZ, countries like India and Nigeria are unique in the latter half of the 2020s and 2030s as being the most productive workforces in the world and, yes, demographics really matter here. So for a young Indian, Nigerian, Indonesian, Brazilian or Malaysian in 2021 this really is the right time to start a career in data science since that could lead to bigger and brighter things.

So let's start the list of generic skills that I think matter the most for the future data scientists and students now studying programming and related fields of skills that are transferable to the innovation boom that is coming.

1. Machine Learning

Machine learning is basically a branch of artificial intelligence (AI), that has become one of the most important developments in data science. This skill focuses on building algorithms designed to find patterns in big data sets, improving their accuracy over time.

The more data a machine learning algorithm processes, the “smarter” it becomes, allowing for more accurate predictions. 

Data analysts (average U.S. salary of $67,500) aren’t generally expected to have a mastery of machine learning. But developing your machine learning skills could give you a competitive advantage and set you on a course for a future career as a data scientist.

2. Python

Python is often seen as the all-star for an entry into the data science domain. Python is the most popular programming language for data science. If you’re looking for a new job as a data scientist, you’ll find that Python is also required in most job postings for data science roles. 

Why is that?

Python libraries including Tensorflow, Scikit-learn, Pandas, Keras, Pytorch, and Numpy also appear in many data science job postings.

According to SlashData, there are 8.2 million active Python users with “a whopping 69% of machine learning developers and data scientists now using Python".

Python syntax is easy to follow and write, which makes it a simple programming language to get started with and learn quickly. A lot of data scientists actually come from backgrounds in statistics, mathematics, or other technical fields and may not have as much coding experience when they enter the field of data science. Since BigData and AI are exploding, the Python community is of course as you know large, thriving, and welcoming. 

 A library in Python is a collection of modules with pre-built code to help with common tasks. The number of related libraries to Python is staggering to me.

You may want to familiarize yourself with what they actually do:

Data Cleaning, Analysis and Visualization

• NumPy: NumPy is a Python library that provides support for many mathematical tasks on large, multidimensional arrays and matrices.

• Matplotlib: This library provides simple ways to create static or interactive boxplots, scatterplots, line graphs, and bar charts. It’s useful for simplifying your data visualization tasks.

• Pandas: The Pandas library is one of the most popular and easy-to-use libraries available. It allows for easy manipulation of tabular data for data cleaning and data analysis.

• Scipy: Scipy is a library used for scientific computing that helps with linear algebra, optimization, and statistical tasks.

• Seaborn: Seaborn is another data visualization library built on top of Matplotlib that allows for visually appealing statistical graphs. It allows you to easily visualize beautiful confidence intervals, distributions and other graphs.

• Statsmodels: This statistical modeling library builds all of your statistical models and statistical tests including linear regression, generalized linear models, and time series analysis models.

• Requests: This is a useful library for scraping data from websites. It provides a user-friendly and responsive way to configure HTTP requests.

Then there are the Python libraries more related to machine learning itself.

• Tensorflow: Tensorflow is a high-level library for building neural networks. Since it was mostly written in C++, this library provides us with the simplicity of Python without sacrificing power and performance.

• Scikit-learn: This popular machine learning library is a one-stop-shop for all of your machine learning needs with support for both supervised and unsupervised tasks.

• Keras: Keras is a popular high-level API that acts as an interface for the Tensorflow library. It’s a tool for building neural networks using a Tensorflow backend that’s extremely user friendly and easy to get started with.

• Pytorch: Pytorch is another framework for deep learning created by Facebook’s AI research group. It provides more flexibility and speed than Keras.

So as you can see Python is a great foot-in-the-door skill that's related to entering the field of data science.

3. R, A Great Programming Language for Data Science in Industry

R is not often mentioned necessarily with data science. Here's why I think it's important.

R is another programming language that's widely used in the data science industry. One can learn data science with R via a reliable online course. R is suitable for extracting key statistics from a large chunk of data. Various industries use R for data science like healthcare, e-commerce, banking and others.

For example, a Harvard certificate in data science has a section on R.

R’s open interfaces allow it to integrate with other applications and systems. As a programming language, R provides objects, operators and functions that allow users to explore, model and visualize data.

As you may know, machine learning is entering the finance, banking, healthcare and E-commerce sectors more and more.

R is more specialized than Python and as such might have higher demand in some sectors. R is typically used in statistical computing. So if you are technically minded R could be a good bet because R for data science focuses on the language’s statistical and graphical uses. When you learn R for data science, you’ll learn how to use the language to perform statistical analyses and develop data visualizations. R’s statistical functions also make it easy to clean, import and analyze data. So if that's your cup of tea, R is great for finance at the intersection of data science.

4. Tableau for Data Analytics

With more data comes the need for better data analytics. The evolution of data science workers really is a marvel to behold. In a sense data science is nothing new and is just the practical application of statistical techniques that have existed for a long time. But honestly I think data analytics, and more Big Data changes how we can visualize and use data to drive business outcomes.

Tableau is an in-demand data analytics and visualization tool used in the industry. Tableau offers visual dashboards to understand the insights quickly. It supports numerous data sources, thus offering flexibility to data scientists. Tableau offers an expansive visual BI and analytics platform and is widely regarded as the major player in the marketplace.

It's worth taking a look at if data visualization interests you. Other data visualization tools might include PowerBI, Excel and others.

5. SQL and NoSQL

Even in 2021, SQL has a surprisingly common utility for data science jobs. SQL (Structured Query Language) is used for performing various operations on the data stored in the databases like updating records, deleting records, creating and modifying tables, views, etc. SQL is also the standard for the current big data platforms that use SQL as their key API for their relational databases.

So if you are into databases, the general operations of data, data analytics and working in a data-driven environment SQL is certainly good to know.

Are you good at trend spotting? Do you enjoy thinking critically with data? As data collection has increased exponentially, so has the need for people skilled at using and interacting with data to be able to think critically, and provide insights to make better decisions and optimize their businesses.

Becoming a data analyst could be more enjoyable than you think, even if it lacks some of the glamor and hype of other sectors of data science.

According to Coursera, data analysis is the process of gleaning insights from data to help inform better business decisions. The process of analyzing data typically moves through five iterative phases:

• Identify the data you want to analyze

• Collect the data

• Clean the data in preparation for analysis

• Analyze the data

• Interpret the results of the analysis

6. Microsoft PowerBI

With Azure doing so well in the Cloud, Microsoft's PowerBI is good to specialize in if you are less interested in algorithms and more interested in data analytics and data visualization. So what is it?

Microsoft Power BI is essentially a collection of apps, software services, tools, and connectors that work together to work on our data sources to turn them into insights, visually attractive, and immersive reports. 

Power-Bi is an all-in-one high level tool for the data analytics part of data science. It can be thought of as less of a programming-language type application, but more of a high level application akin to something like Microsoft Excel.

If you are highly specialized in PowerBI it's likely you'd always be able to find productive work. It's what I would consider a safe bet in data science. While it's considered user friendly, it's not open source, which might put off some people.

7. Math and Statistics Foundations or Specialization

It seems only common sense to add this but if you are interested in a future with algorithms or deep learning, a background in Math or Statistics will be very helpful. Not all data scientists will want to go in this direction but the data scientist will be expected of course to understand the different approaches to statistics — including maximum likelihood estimators, distributors, and statistical tests — in order to help make recommendations and decisions. Calculus and linear algebra are both key as they’re both tied to machine learning algorithms. 

The easiest way to think of it is that Math and Stats are the building blocks of Machine Learning algorithms. For instance, statistics is used to process complex problems in the real world so that data scientists and analysts can look for meaningful trends and changes in data. In simple words, statistics can be used to derive meaningful insights from data by performing mathematical computations on it. Therefore the aspiring knowledge worker student of data science will want to be strong in Stats and Math. Since many algorithms will be dealing with predictive analytics, it will also be useful to be well-grounded in probability.

8. Data Wrangling

The manipulation of data or wrangling is also an important part of data science, e.g. data cleaning. Data manipulation and wrangling make take up a lot of time but ultimately help you in taking better data-driven decisions. Some of the data manipulation and wrangling generally applied is – missing value imputation, outlier treatment, correcting data types, scaling, and transformation. This in general makes Data Analysis possible.

Data wrangling is essentially the process of cleaning and unifying messy and complex data sets for easy access and analysis. With the amount of data and data sources rapidly growing and expanding, it is getting increasingly essential for large amounts of available data to be organized for analysis. There are specialized software platforms that specialize in the data analytics lifecycle.

The steps of this cycle might include:

• Collecting data: The first step is to decide which data you need, where to extract it from, and then, of course, to collect it (or scrape it).

• Exploratory data analysis: Carrying out an initial analysis helps summarize a dataset’s core features and defines its structure (or lack of one).

• Structuring the data: Most raw data is unstructured and text-heavy. You’ll need to parse your data (break it down into its syntactic components) and transform it into a more user-friendly format.

• Data cleaning: Once your data has some structure, it needs cleaning. This involves removing errors, duplicate values, unwanted outliers, and so on.

• Enriching: Next you’ll need to enhance your data, either by filling in missing values or by merging it with additional sources to accumulate additional data points.

• Validation: Then you’ll need to check that your data meets all your requirements and that you’ve properly carried out all the previous steps. This commonly involves using tools like Python.

• Storing the data: Finally, store and publish your data in a dedicated architecture, database, or warehouse so it is accessible to end users, whoever they might be.

Tools that might be used in Data wrangling are: Scrapy, Tableau, Parsehub, Microsoft Power Query, Talend, Alteryx APA Platform, Altair Monarch or so many others.

9. Machine Learning Methodology

Will data science become more automated? This is an interesting question. At its core, data science is a field of study that aims to use a scientific approach to extract meaning and insights from data. Machine learning, on the other hand, refers to a group of techniques used by data scientists that allow computers to learn from data. 

Machine learning are techniques that produce results that perform well without programming explicit rules. If data science is  the scientific approach to extracting meaning and insights from data, it is really a combination of information technology, modeling, and business management. However machine learning or even deep learning actually often does the heavy lifting.

Since there is just a massive explosion of big data, data scientists will be in high demand for likely the next couple of decades at least. Machine learning creates a useful model or program by autonomously testing many solutions against the available data and finding the best fit for the problem. Machine learning leads to deep learning and is the basis for artificial intelligence as we know it today. Deep learning is a type of machine learning, which is a subset of artificial intelligence.

So if a data scientist student is interested in working on AI, they will need a firm grounding in machine learning methodology. While machine learning requires less computing power, deep learning typically needs less ongoing human intervention. They are both being used to solve significant problems in smart cities and the future of humanity.

10. Soft Skills for Data Science

To work in technology soft skills can be huge differentiators when everyone on the team has the same level of knowledge. Communication, curiosity, critical thinking, storytelling, business acumen, product understanding and being a team player among many other soft skills are all important for the aspiring data scientist and these should not be neglected.

Ultimately data scientists work with data and insights to improve the human world. Soft skills are a huge asset for a programming student that wants to be a manager one day or even to transition to a more executive role later in life or become an entrepreneur after their engineering life is less dynamic. You will want to especially work on:

• Empathetic leadership skills

• Power of observation that leads to insight into others

• Good communication

Having more polished soft skills can also obviously enable you to perform better on important job interviews, in critical phases of projects and to have a solid reputation within a company. All of this greatly enhances your ability to move your career in data science forward or even work at some of the top companies in the world.

Conclusion

A career in data science is incredibly exciting when AI and Big Data permutate our lives more than ever before. There are many incredible resources online to learn about data science and particular career paths for programming, machine learning, data analysis and AI.

Finally whether you choose data science or machine learning will depend on your aptitude, interests and willingness to get post graduate degrees. They can be summarized by the following:

Skills Needed for Data Scientists

• Statistics

• Data mining and cleaning

• Data visualization

• Unstructured data management techniques

• Programming languages such as R and Python

• Understand SQL databases

• Use big data tools like Hadoop, Hive and Pig

Skills Needed for Machine Learning Engineers

• Computer science fundamentals

• Statistical modeling

• Data evaluation and modeling

• Understanding and application of algorithms

• Natural language processing

• Data architecture design

• Text representation techniques

I hope this has been a helpful introductory overview meant to stimulate students or aspiring students of programming, data science and machine learning while giving a sense of some key skills, concepts and software to become familiar with. The range of jobs in the field of data science is really quite astounding, all with slightly different salary expectations. The average salary for a data scientists in Canada (where I live) is $86,000, which is $5 million Indian Rupees (50 lakhs) for example.

Share this article with someone you know that might benefit from it. Thanks for reading. 

This article has 3,215 words. 

This article was originally published on Data Science Central but has been retracted due to breach of verbal agreement contract.