Top Skills for a Data Science Team

“We’re entering a new world in which data may be more important than software.” - Tim O’Reilly

Just two decades ago, Data Science term and data scientists didn’t exist. Of course, some people cleaned, organized and analyzed information — but a data science professional in the way we understand it today is a relatively new (and vaunted) occupation. Modern data scientists combine skills that merge the technical know-how of an analytical expert with the curiosity and problem-solving abilities of a scientist which results in one invaluable profession.

From Microsoft and Facebook to Zomato and Zara - everyone is now looking for Data Science and Machine Learning skilled specialists. Many companies start to create their in-house teams, while others hire external professionals. Some will be developing complex machine learning prediction algorithms in python, others will be producing simple charts in Excel.

The global machine learning market is expected to reach $20.83 Billion by 2024. While, according to Glassdoor, the average pay scale of a data scientist is twice higher than those of an average computer programmer. Data Science is one of the fastest-growing fields along with its job opportunities.

But how is data science seeing so much growth? The answer lies in the wide range of its applications. In this field they are endless – from everyday sales prediction to all the way up to self-driven cars and personal assistants, powered by Data Science. No wonder every organization craves a talented Data Scientist .

They’re part mathematician, part computer scientist, and part trend-spotter. And, because they straddle both the business and IT worlds, they’re highly sought-after and well-paid. Who wouldn’t want to be one?” (SAS Insights)

Even though the jobs in the field of data science are seeing growth, there is still a massive shortage of skilled data scientists.

In this article we would like to take a look at the key techniques a solid analytics team should know to use.

1. Machine Learning

For a data scientist, knowledge in machine learning is essential. It is mainly used to build predictive models. For example, you want to predict the number of customers you will have in the next month by using the data from the previous one. You will need to use machine learning algorithms to count that.

A good data science specialist should know the simplest linear and logistic regression model as well as advanced ensemble models like Random Forest, XGBoost, CatBoost, etc. It’s also useful to understand the code for these algorithms (which actually takes 2-3 lines) and, even more importantly, know how they work.

2. Turning business questions into testable hypotheses

A PhD in Data Science is of little use to a company if the graduate can’t solve data problems. Basically, this means taking questions and concerns from decision makers and converting them into statistically testable theories and hypotheses.

For example, a key stakeholder asks: ‘Should we introduce a promotional offer for product X?’

Which information is needed to answer this question? What do we need to predict? What data is needed to make that prediction? And what other factors should we take into account? Once these and many other questions are set up we need to think about how we're going to build a model and how we're going to tackle any uncertainty.

3. Programming

Programming is at the core of Data Science. It is necessary to turn any unprocessed data into useful information. Although a data scientist might have access to a variety of programming languages such as Julia, Scala, and Swift, etc, Python and R have been the favorites for quite some time.

They include a vast collection of third-party libraries, clean and comprehensible syntax, and the efficiency of the code along with the effective utilization of the resources.

4. SQL

Programming languages in Data Science are important, but what’s equally necessary is the ability to extract and handle raw and untouched data from hundreds of sources. SQL or Structured Query Language is what transforms the silos of data into useful bits of information, which are then used by the developers. A data scientist skilled in both can smartly make use of the various libraries available in, say, Python or R, to achieve results faster with SQL.

SQL includes various advanced data manipulation techniques that allow developers to restructure the data to their liking and process it.

5. The ability to sort messy data

Data nearly always comes in a messy format. You might have been sent a lot of miscellaneous CSVs, or a wrongly formatted automatic report. Or maybe you're scraping data from a website and you've got a JSON file as a source of information.

The key things to consider when cleaning messy data are:

Column formats - are dates coded as dates? Or do text variables need to be converted into binary 0s and 1s?

Missing values - are there numerous NAs? Are you planning to exclude these or impute values? Are there text strings mixed in amongst numeric values?

Data errors - when plotting variables do you notice huge spikes or zeros?

Long / wide format - do you need to gather your data into one long format?

6. Correlation and regression analysis

Understanding causal relationships and correlations between variables to provide robust recommendations.

What impact might price changes have on KPIs? Are your marketing channels working well together and driving engagement? Which parts of your website are driving click-throughs?

Regression models help to understand the impact of various factors on outcomes. They allow us to predict what is likely to happen given your decisions.

7. Visualisations

Data visualisations allow you to spot patterns and quickly see relationships in your data.

Visualisations are mainly used to explore data and to communicate results effectively.

Tableau, PowerBI, d3, R and good old Excel are the examples of great tools to visualize data.

Visualisations then can be customised across every dimension. Some platforms allow you to create rough and ready plots to visualise model outputs or slick dynamic maps as a part of a web-based dashboard.

8. Conducting cluster analysis

Cluster analysis is a great technique used to understand different segments. These could be different customer groups, cities, website visitors, active audience, etc. Such learning methods as kmeans look for natural separations in the data given. Subjects are divided into different groups and each group can be described by the prevalence of certain characteristics set.

There are huge libraries available in R and python, covering everything from the most basic clustering to highly advanced machine learning algorithms.

9. Big Data

We are generating as much data as 2.5 Quintillions per day! Due to the rise of the internet, social media and IoT there has been a fast boom in the rate of data we are using. This data is high in volume, velocity, and veracity which are the 3V’s of Big Data.

Organizations have been overwhelmed with such a large amount of data and they are rapidly adopting Big Data Technology so that this data can be stored properly and efficiently and used when needed.

10. NLP, Neural Networks and Deep Learning

AL is evolving at a high rate bringing more new technologies such as Natural Language Processing, Neural Networks, and Deep Learning . Knowing how to use them properly can help automize, fasten and simplify many processes in Data Science.

• NLP plays a key role in managing and processing automated interaction between humans and computers (chatbots, voice assistants, email filtering tools, language translators, and more)
• Artificial Neural Network simulates the network of neurons in a human brain and helps solve complex tasks. Some of its real-life applications are: stock values, image compression, face and speech recognition.
• Deep Learning uses Artificial Neural Networks on an even deeper scale with numerous layers to help with fraud detection, pixel restoration, coloring black & white images, etc.

11. DBMS or Database Management System

It essentially supports SQL that allows the developers to create, change and view structured relational data, but DBMS adds the creation, management, and manipulation of databases and tables where the data is stored.

A DBMS can act as a bridge between an application and the data requested, it can access and even modify the structure of the data at micro level, backup and restore databases again.

12. Storytelling

Imagine watching a cricket match stats, where the runs scored on each bowl are shown in the form of a table. Do you think it is easy to get important information from this? A bar chart of runs scored in each over seems better, right? It is not in human nature to understand blocks unless they are interactive.

Storytelling is the utmost important acquired skill by a data scientist. Most clients search for specialists who are able to explain and engage.

13. Clear recommendations

Communicating results is a skill that takes time to acquire. Everyone of us is so interested in learning new analytical techniques that neglect to build up our communication skills as well. And communicating the results of statistical analysis is not easy. Even simple averages are often not easily understood.

Here the key concepts companies want their data scientists to use:

• Keeping the focus recommendation-led. It means without jumping straight into the methodology and starting with what judgements can be drown from the analysis.
• Using simple plots
• Incorporating some level of uncertainty. It is often easier to communicate single point estimate recommendations. But you should strive to incorporate uncertainty into your feedback. This can be most easily done by giving 'best', 'likely' and 'worst' cases scenarios.

The domain of Data Science brings a variety of scientific tools, processes, algorithms, and knowledge extraction systems from structured and unstructured data alike, in order to identify meaningful patterns in it.

As the field of Data Science becomes wider, the need for Data Science professionals increases. The goal behind this write-up was to familiarize you with some of the latest Data Science trends, skills that are in-demand in 2021, or simply provide you with some insights in how to improve your existing practices.

At Utah Tech Labs, we offer custom made decision engine solutions to small, mid-size and large enterprises. We follow the latest trends while being attentive to changes in the market to make sure we deliver the latest technology and outstanding results.

For more information about Data Science services at Utah Tech Labs: https://www.utahtechlabs.com/data-science