Do you know what the sexiest job of the 21st century is? Surprisingly, it’s that of a data scientist. Don’t believe us? Try googling it, and you’ll be amazed.
A data scientist makes discoveries while swimming in data, which is, of course, a glamorous way to describe the duties. They are responsible for extracting the maximum value out of humongous volumes of data and using it to deliver actionable insights to organisations.
Jobs in data analytics dominates a majority of the in-demand jobs in 2020 and has the highest proportion at 33.7% when considering the information technology (IT) domain. It is top among the highest paying IT jobs in the world and doesn’t show any sign of slowing down for the foreseeable future (for a decade at least).
In this article, we’ll discuss the critical skills required for jobs in data analytics, top career paths you can choose, and the tips and tricks to kickstart your career in data analytics.
What Are the Skills Required for Jobs in Data Analytics?
1. Analytical and Creative Thinking
This may sound cliché, but when looking for jobs in data analytics, you must have an analytical mindset and the capability to creatively find solutions with the backing of data. Clearly, the first obvious solution is not always the best one.
For example, if you’re on the quest of solving a problem, you may come across multiple solutions and numerous means to execute them. With an analytical mindset, you will be able to choose a solution that takes the least time, consumes the least resources and produces the most profitable result out of all.
A good data analyst must have an eye for detail and problem-solving abilities that will enable them to find easy solutions to complex problems. Only when your creativity, curiosity and tenacity are combined, you’ll be able to communicate the “big picture” and steer clear of uncertainties.
2. Data Visualization Skills
Data visualization skills refer to your ability to identify patterns, trends, and correlations and present them in graphical or pictorial format. As many of the text-based data can go unnoticed, you must have what it takes to visualise data in the form of charts or graphs, so that people can better understand their significance.
Thanks to technology, an average human’s attention span is at 8 seconds currently. Hence, it is natural for us to get distracted or uninterested when presented with long columns of numbers or texts. Additionally, the human brain is designed to process images faster than texts.
Regardless of your data analyst job description, the ability to visualize and communicate insights is an invaluable skill any organisation will be keen to pay for. In short, a data scientist must act as a translator and convey the meaning of data sets using graphs and charts.
3. Communication Skills
Unless you are the Queen’s Guard, every profession you’ll ever consider will require excellent communication skills to excel, and jobs in data analytics are no different. Although the roles of a data analyst or scientist are primarily technical, there will always be instances where explaining the results and potential of a project is critical for its success.
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In such instances, you must have the critical communication skills that will allow you to break down your findings into digestible and conceivable information that will give out more answers than attract more questions.
Similarly, communication skills also go in hand with data visualization skills, as in essence, visuaization is a form of communication that creatively (and graphically) describes data. Communication skills are especially critical for freshers as employers will be keener to quickly know how you can bring value to the table.
Python is an internationally recognised programming language to handle complex data sets primarily for two reasons. One, it as an impressive number of data-oriented feature packages that will speed up data processing; And two, it’s easy to learn – thanks to its readability and clear syntaxes.
However, knowing your way around Python isn’t always considered as a prerequisite to land jobs in data analytics. But having a solid understanding of it will give you an upper hand in interviews and big data-related discussions.
There are numerous drag and drop tools available for machine learning pipelines like KNIME, Weka, MLJAR, and RapidMiner. But again, knowing Python will grant you more flexibility to customise your analytics models.
5. SQL (Structured Query Language)
SQL is a programming language extensively used to manage data stored in a relational database management system (RDBMS). ANSI (American National Standards Institute) considers SQL as the standard language for every RDBMS.
Simply put, SQL is a query language, used to retrieve information from databases. In the case of big data, data may come from multiple sources, in varying formats and volumes. Data scientists will require to store, manage or delete data – which is made possible by SQL.
However, do note that SQL can’t be used to write full-fledged applications. It is a simple, but a powerful language used to fetch data from databases. SQL is extensively used in proprietary tools such as MySQL, PostgreSQL, and Microsoft Access.
R is a free software environment and a programming language used for statistical computing and data mining. It also allows users to add extra functionality with the help of new functions. It is a simple and effective programming language and has components like loops, input-output, and user-defined recursive functions like in the C++ programming language.
More precisely, R isn’t just used to analyse data; instead, it is used to create applications that can perform statistical analysis. R has a graphical user interface and is also used to build data collection, analytical and clustering models.
For data analyst jobs, being proficient in R programming language is a prerequisite as they will help you to gather, clean, analyse, and visualize data.
7. Hadoop or Spark
Around 90% of a data analyst’s job will revolve around cleaning, analysing and visualising data. To aid these processes, frameworks such as Hadoop and Spark are used. Hadoop is an open-source framework that enables you to process large volumes of data across clusters of computer systems.
The e-commerce industry extensively uses Hadoop to analyse large volumes of structured and unstructured data to better understand the needs and wants of the customers. To put that in perspective, Amazon sells more than 4000 items per minute in the US alone.
Also, as the majority of security breaches come with early warnings, analysing data in Hadoop can aid in identifying and initiating proactive measures to counter them. Thus a framework as scalable and robust as Hadoop is essential.
Similarly, Spark is a big data processing framework that supports a wide range of tasks, such as processing streaming data and fog computing. It is essentially a fast cluster computing system for big data and has the ability to distribute tasks across multiple computer systems.
Excel spreadsheets are powerful tools for organizing and manipulating data. Even after 30 years of its creation, Microsoft Excel still remains one of the most extensively used applications for data analysis.
The application consists of multiple formulas that make data analysis a breeze. Although Excel spreadsheets don’t fulfil all the requirements for data analysis, it’s an affordable and reliable tool to start with. It can significantly deepen your understanding of the data analytics process and can quickly organise raw data into a readable format.
Here’s a list of a few of the data analysis functions in Excel you can start learning right now.
Top Careers That Require the Knowledge of Data Analytics
To be precise, the skills required and responsibilities associated with different job roles in data analytics overlap as the ability to interpret “data” is a common prerequisite. Broadly speaking, data analytics career opportunities can be grouped into four major roles as follows.
1. Data Analyst
A data analyst is an individual who extracts information from a pool of complex data sets. For this, they use methodologies such as data cleaning, data conversion and data modelling. To be precise, a data analyst is responsible for performing descriptive and diagnostic analytics.
They make recommendations about the methods and means by which an organisation can obtain and analyse data. Here are some of the obligations of a data analyst.
- Aggregating and analysing data from primary and secondary sources.
- Identifying patterns, trends and correlations.
- Reporting results to the stakeholders or relevant members of the team.
- Maintaining and designing databases and data systems.
- Filtering and cleaning data and correcting errors.
A data analyst earns around US$74,997 per year in the US.
2. Data Scientist
A data scientist is responsible for working closely with the stakeholders of an organisation. They are problem solvers and help companies determine how data can be used to achieve their goals. They use sophisticated analytical techniques, statistical methods and machine learning for predictive and prescriptive analytics.
Data scientists are also responsible for creating predictive models and algorithms to extract data and designing data modelling processes. Do note that a data scientist will possess all the skills a data analyst has, along with additional knowledge, especially about predictive and prescriptive analytics.
Here are some of the typical responsibilities and duties of a data scientist.
- Pulling, merging and analysing data
- Discovering new algorithms and models to provide solutions and build new programs
- Performing exploratory data analysis (EDA)
- Seeking questions and finding solutions
- Recommending cost-effective modifications to existing strategies
A data scientist earns around US$121,655 per year in the US.
3. Data Engineer
Data engineers understand programming better than data scientists. They are responsible for data ingestion and transforming data into a useful format for further analysis. They prepare the big data infrastructure for the data scientists to analyse and may also create big data warehouses.
As they focus more on design and architecture, unlike data scientists, they generally aren’t expected to know much about machine learning or analytics. The general responsibilities of a data engineer are as follows.
- Identifying ways to improve quality, efficiency, and reliability of data
- Developing, testing, and maintaining architectures
- Developing complex scripts and data pipelines
- Creating tools to enhance the flow of data
- Developing proof of concepts and prototypes
A data engineer earns around US$129,415 per year in the US.
4. Data Storyteller
A data storyteller is responsible for developing a narrative around a given data set and its visualization. They are responsible for mapping how the metrics of the analysed data connect with the objectives of a company and craft an engaging story to present the findings in a creative manner.
Simply put, data storytelling is the art of translating data analysis into simpler terms, so that the results are evident even for the non-technical audience. As most of the principal decision-makers of an organisation may not have the ability to interpret data, a data storyteller explains how data insights can be used to make better decisions.
Ideally, a data storyteller must have,
- Ability to tell persuasive and engaging stories (obviously)
- In-depth knowledge of the organisation’s needs and strategies
- Firm understanding of data concepts
In most companies, the role of a data storyteller is often performed by the data scientists. So, a prerequisite of becoming a data scientist is mastering the art of storytelling.
How to Kickstart a Career in Data Analytics?
1. Adopt an Analytical Mind-Set
Data analysts base their decisions on data. Even though they may have various hypotheses to test, ultimately they approach a question without any preconception and use data to find answers.
When pursuing a career in data analytics, such a mindset to see solutions as a result of data analysis is crucial. A simple example would be relying on weather reports than your gut instincts or setting the cooking time of pasta rather than wildly estimating.
2. Get a Bachelor’s Degree in Computer Science or Math
It would be unfair if we say getting a bachelor’s degree in computer science or math is a prerequisite to becoming a data analyst. Even though having a degree is a great way to increase your credibility, many of the companies, including Google and Apple, don’t make it mandatory.
Instead, you can learn a lot from watching YouTube videos, attending online data analytics courses, reading articles from industry leaders, and so on. What a lucky time to be alive!
3. Join a Peer Group
Love it or hate it, networking and joining peer groups are the easiest ways to stay up-to-date with changing trends and open to new career opportunities. You don’t have to be present in any groups physically. There are numerous online communities, including Quora, LinkedIn groups and Reddit, where you can share your views and learn from experts.
4. Learn to Code
One of the basic and universal skills a data analyst requires is coding. You must be able to read, write, and analyse code. Start with any one tool or language, and once you are proficient in using a tool, learning the next one will be a lot easier.
Although the tools and languages used may change every few years, their basics will probably remain the same. Fortunately, there are plenty of online courses in Python, SQL, R, and Excel, which will teach you right from the start.
5. Develop Your Soft Skills
Soft skills are essential for any job you’ll ever consider. You need to maintain certain etiquettes while communicating with peers and must have the required communication skills to prove a point in meetings or while sharing your ideas.
6. Get a Data Analytics Certification
Data analytics certification courses will bring more structure to the learning process. You’ll have a predefined learning path and can monitor your progress. These certifications will also make your resume more attractive as the issuer vouch for your expertise.
Again, getting a data analytics certification isn’t a prerequisite to becoming a data analyst. Instead, you can rely on YouTube and other means (previously discussed). The only difference would be that you’ll lack certification to showcase the skills and knowledge you acquired.
7. Build Your Own Projects
Building your own projects will help in sharpening your skills and demonstrating your expertise to future recruiters. Since new tools are coming out continuously, experimenting with them will be a great way to learn them and stay ahead of trends.
Building your own projects isn’t as hard as it may seem. Here are a few points to get started.
- Identify a dataset which you find interesting enough to create charts about.
- Brainstorm a list of questions you want the dataset to answer.
- Use tools like Jupyter Notebooks to analyse the dataset.
- Make use of GitHub to store your notebooks.
8. Participate in Competitions
Participating in competitions can put your skills to test. You can also understand how individuals around the world are using their skills and learn a lot in the process. Online communities like Kaggle and Analytics Vidhya can be your go-to for this.
9. Get an Entry-Level Data Analyst Job
Almost every industry requires the expertise of data analysts. You can try for an entry-level job as a financial analyst, market researcher or budget analyst and progressively learn the tricks of the trade.
The easiest way to land jobs in data analytics is by entering an institution by the role of an intern. Try job portals like Indeed, Glassdoor, and LinkedIn. The key is to try until you land a job, even if it means filling hundreds of applications.
10. Get a Master’s Degree in Data Analytics
Again, having a master’s degree isn’t a prerequisite to becoming a data scientist. However, a master’s degree will better prepare you to conquer the field of data analytics with ease and bring more structure to the learning process. You’ll also have access to tools provided by the university and better guidance, unlike learning on your own.
How to Make the Transition From Data Analyst to Data Scientist
- Solidify your knowledge of math and statistics.
- Get a better understanding of the tools.
- Become a persuasive speaker and storyteller.
- Get creative with problem-solving.
- Work with people who know more than you.
- Get skilful in predictive analytics.
Although many might disagree with the notion that “data is the new oil”, the duties of a data science professional are always bound to empower decisions and innovations. Of course, developing the skills to become one won’t happen overnight – depending on your prior experience, it may take months or even years.
With organisations emphasizing more on the skills acquired, rather than college degrees, you can always upskill by listening to thought leaders, attending data analytics certification courses and maintaining a constant presence in online forums.
Jobs in data analytics require only your passion for unlocking solutions with data and a perpetual keenness to learn as requisites. Adopt an analytical approach towards life and do as many personal projects as possible – your dream to become a data scientist is certainly within your reach.