Building a successful career in data science comes with a lot of benefits and life-changing opportunities. Unfortunately, we live in a society that is governed but also driven by money and social status. Working your way up the ladder is known to improve the quality of your life. It offers security and a feeling of accomplishment. People have been able to transform their life from zero to high-flying careers. 

If you’re a Data Scientist and you’re setting your 2023 goals to improve and build your career, you’ve landed on the right page. This article will show you how you can go about it.

• What is Data Science?
• How do you Create a Successful Career in Data Science?
• What is the Most Rewarding Part of Being a Data Scientist?
• How can I Become an Awesome Data Scientist?
• How can I be Passionate About Data Science?
• Is Data Science a Rewarding Career?
• Is Data Science Hard?
• What’s Next After Data Science?
• What are Some Popular Data Science Job Roles
• What are the Key Data Science Concepts?
• Is Data Scientist a Stressful Job?
• What Motivates you to Become a Data Analyst?
• Is Data Science the Future?

What is Data Science?

Data science is a broad field that refers to the collective processes, theories, concepts, tools and technologies that enable the review, analysis, and extraction of valuable knowledge and information from raw data. It is geared towards helping individuals and organizations make better decisions from stored, consumed and managed data.

Read Also: Can you Make Money From Coding?

Data Science blends various tools.  It makes use of algorithms and machine learning principles with the goal to discover hidden patterns from the raw data. 

A data scientist basically works with raw data using various tools and algorithms to solve critical data analytic problems.

While other experts take care of building software (which is used to collect data from multiple sources), coding models, and application of data collection tools and methods respectively, a data scientist takes care of collecting and storing data pulled in from several sources, analyzing and visualizing the data, and finally deriving insights from it. 

A data scientist spends a lot of time in the processing, cleaning, and munging data. This process requires persistence, statistics, and software engineering skills.

Soft skills are great and are very essential in growing your career. However, to define yourself as a Data Scientist, it requires hard skills such as analysis, data visualizations, machine learning, statistics, and more. Pairing with soft skills such as being a problem solver, eager and self-motivated learner along with critical thinker will help you excel in becoming a successful Data Scientist. 

The tech world is growing at such a fast rate, the only thing stopping you from building your career in this sector is the qualifications proving you offer these hard skills. 

How do you Create a Successful Career in Data Science?

Data Science is ranked #2 among the top 20 in-demand skills, and as per Glassdoor data, 2021 alone witnessed over 37,000 new job offerings for machine learning engineers, data analysts, data scientists, and business analysts, among other roles.

The ever increasing demand for Data Science experts hasn’t been met given the lack of skilled and industry-ready candidates with a knack for harnessing the power of data. Young professionals are recommended to understand and develop the skills needed for various data science roles so they can tap into this high-demand domain and build a successful career.

Here are some essential tips for starting a career in Data Science:

1. Choose the right role
Working in the Data Science domain can involve a variety of roles ranging from those of data analysts, data scientists, data visualization experts, data engineers, machine learning experts and more. When starting a career in the datascape, it is critical to take on a role that aligns with your skill set, educational background, work experience and interests.

2. Update your skill set with courses
After deciding on a role, the next step is to understand it deeper by learning the nuances of that field, which will help you advance in that role. A great way of learning these is to engage with real-world case studies through programs and courses on platforms like Great Learning.

The platform presents learning in a unique blended format with live and recorded classes, combining an immersive classroom experience with online learning. The hands-on pedagogy gives learners a chance to gain job-ready skills through projects, assignments, and real-life problem-solving scenarios.

3. Choose to learn a Data Science tool/language
Selecting tools/languages may be a fundamental problem for Data Science enthusiasts. The answer is to select a mainstream tool/language to start a career in Data Science. To help with this, Great Learning offers an array of certificate and degree programs in Data Science, ranging from basics such as tool/language selection to advanced post-graduate degrees.

4. Find and join peer groups
Finding like-minded peers who are interested in the same field as you will help you stay motivated and explore more of your potential in the field. Additionally, peers can provide a fresh perspective to your problem-solving efforts, as well as enable networking avenues that facilitate a meaningful career journey. You can find the right peer network for yourself through platforms like Great Learning as well.

5. Focus on practical applications
Practical and applied learning are more critical than just theoretical knowledge as it allows you to gain practical experience for more meaningful career outcomes. Real-life applications also add to the repertoire of a Data Science professional along with their current skill set. It is only when you start actively contributing with the applied use cases that your relevance and value creation within an organization is compounded.

6. Learn communication and soft skills
A Data Science professional may wear multiple hats. Right from exploring the data to get some early insights to modeling for prediction or pattern generation, a lot of critical thinking is required. This is often followed by the ability to better communicate insights to key stakeholders so that they can make informed decisions. To become a well-rounded professional, you also need to develop the right kind of soft skills, such as the ability for critical thinking, listening, persuasive communication, and problem-solving.

What is the Most Rewarding Part of Being a Data Scientist?

Even if you’re considering a change in your career or just starting your career in this field with no background in data science, it still is a great career option. If you want to know the advantages of being a data scientist, then we’ve compiled a variety of the perks you’d receive.

1. Sexiest job of the century

A Harvard Business Review article says that “data scientist” is the sexiest job of the 21st century. There are many reasons that justify this title. Many old industries that work in data field have replaced their job listing titles with “data scientist.” And not just the title either.

Companies that work in finance, statistics, and operational research are taking interest in data science more so than ever. Data Science is driving value to many businesses and the data scientist job trend is growing steadily. Being in the most sought-after job clearly means that an individual’s career growth will be excellent.

2. Freedom to work

If you meet a data scientist and ask what they like best about being a data scientist, their answer would be – freedom. In data science, you’re not bound to work for a particular industry.

One of the best advantages of being a data scientist is that you work with technology and it means you become a part of something that has huge potential. You are free to work on the projects that interest you. And more importantly, you are changing the lives of thousands of people through your work in the field of data science.

3. A chance to work with big brands

As a data scientist, you are open to jobs in companies like Amazon, Apple, and Uber. For example, Amazon uses data science to sell products by recommending them to customers. The data Amazon uses comes from its huge customer base. Apple also uses big data to make decisions for its product features. Uber’s surge pricing is one of the finest examples of how big companies use data science.

4. The payoff is handsome

The median salary of a data scientist in the US is around $120,000. Since 2011, 94 percent of the US graduates have found data scientist jobs with an average salary of $114,000. The data scientist job holds the number one position among top 25 best jobs according to a 2016 report and in the current scenario, it still holds the position.

5. Proper training and certificate course

Data scientists, unlike many other IT jobs, do not have to create useless study material for beginners. Many courses in data science are backed by experts with solid experience and knowledge in the field. That’s why motivating yourself to learn about data science and visualization can help you gain more knowledge and skills in this field.

Certified data scientists can expect around 58% pay raise, which is comparatively higher than non-certified professionals who get 35% chances. The road to promotion and resume shortlisting is very clear for certified professionals, but it doesn’t necessarily mean that self-taught data scientists can’t grow.

6. Data science jobs in demand

A data scientist can choose any path – project management, security, system architecture, consultancy etc. For these areas of expertise, there’s a huge demand for knowledgeable professionals. The job growth rate is even more than 100% for some profiles.

For example, the job growth rate for the information security analyst position is 279.69% and same goes for other profiles as well. More than 1.5 million data managers will be in demand by the 2018’s year-end. IBM has predicted that the demand for data scientists will continue to rise.

7. Different roles in the industry

Data Science is exciting, and it requires individuals who fit into different roles to come to solve real-world problems. All around the world, large and smaller organizations are creating data, every single day. But not everyone is taking advantage of the data they generate. To help these companies, each individual with a background in data science is needed at different stages.

They are needed for analysis, translating business problems into easy-to-understand data questions, implementation of a statistical approach to data, and bridging the gap between theories and programming. Not every job offers as much flexibility and endless learning opportunities.

8. A safe career to pursue

New technologies come and go and that’s why people have started believing that everything that shines in the tech world turns out to be a bubble. This is not the case with data science. It doesn’t necessarily mean that a data scientist should stop learning new skills because his job will remain safe forever. We all know that various aspects of today’s technology, including data science, will be automated.

It means some areas will see automated processes but the field of data science will grow and the demand for data scientists will continue to grow. Those who will possess skills and the right mindset will not be thrown out of their positions.

9. Hang out with C-level executives

Data scientists work on many technologies and programming languages. They use many tools to solve business problems, and during the process, they gain the confidence of people from top management. Data scientists work with people from various industries and departments, and as long as they are working on improving their skills, they can be found hanging out with C-level executives.

10. Building a business becomes easy

When you know inside and out of many industries and when you build good contacts and gain the ability to solve real-world business problems, it becomes easy to establish your own business. For those who always dream about building their own business before retirement, their experience, contacts, and knowledge as data scientists can be helpful in their future endeavors.

How can I Become an Awesome Data Scientist?

Data science is the area of study that involves extracting knowledge from all of the data gathered. There is a great demand for professionals who can turn data analysis into a competitive advantage for their organizations. In a career as a data scientist, you’ll create data-driven business solutions and analytics.

Step 1: Earn a Bachelor’s Degree

A great way to get started in Data Science is to get a bachelor’s degree in a relevant field such as data science, statistics, or computer science. It is one of the most common criteria companies look at for hiring data scientists. 

Step 2: Learn Relevant Programming Languages

While a Bachelor’s degree might give you a theoretical understanding of the subject, it is essential to brush up on relevant programming languages such as Python, R, SQL, and SAS. These are essential languages when it comes to working with large datasets.

Step 3: Learn Related Skills

In addition to different languages, a Data Scientist should also have knowledge of working with a few tools for Data Visualization, Machine Learning, and Big Data. When working with big datasets, it is crucial to know how to handle large datasets and clean, sort, and analyze them.

Step 4: Earn Certifications

Tool and skill-specific certifications are a great way to show your knowledge and expertise about your skills.  Here are a few great certifications to help you pave the path:

• Tableau Certification Training Course
• Power BI Certification Course

These two are the most popular tools used by Data Scientist experts and would be a perfect addition to start your career journey.

Step 5: Internships

Internships are a great way to get your foot in the door to companies hiring data scientists. Seek jobs that include keywords such as data analyst, business intelligence analyst, statistician, or data engineer. Internships are also a great way to learn hands-on what exactly the job with entail.

Step 6: Data Science Entry-Level Jobs

Once your internship period is over, you can either join in the same company (if they are hiring), or you can start looking for entry-level positions for data scientists, data analysts, data engineers. From there you can gain experience and work up the ladder as you expand your knowledge and skills.

How can I be Passionate About Data Science?

When interviewing for a data scientist position, the interviewer might ask questions about your interest in the role and what excites you about data science. One question they might ask is, “Why do you want to be a data scientist?” Answering this question successfully can show that you’re passionate, knowledgeable and a good fit for their company.

You can use the following steps to answer this question effectively:

1. State your passion for data science

Companies want employees who approach their work with enthusiasm. Begin by sharing that you’re passionate about data. You can also show your interest by explaining what drew you to the field. For example, you might mention that you enjoy problem-solving and statistical analysis, which led you to a career in data science. When answering, speak sincerely.

2. Mention your background

This question can be an opportunity to highlight your education and discuss any relevant previous roles. Explain how you began working in data science and be specific about how your interests have developed throughout your career. You can share an anecdote that details your interests. For example, you might discuss a project at your previous job where you realized you especially enjoy feature engineering.

3. Talk about technology

Technology is essential to a data scientist’s job, so employers might appreciate knowing that you can adapt to new advancements easily. You can mention a specific emerging technology that excites you, like artificial intelligence, blockchain or machine learning. If possible, tailor your answer to the company because mentioning the technology they use may impress the interviewer.

4. Discuss the value of data science

It’s important to understand the role data plays in making informed business decisions. Show the interviewer you take this connection seriously and want to use your analytical skills to help the company meet its goals. For example, you might mention how automation can cut costs and increase profits. You might even consider sharing a story from a past role. Discussing a time when your analysis translated to a business victory shows that your work can benefit employers.

5. Connect your answer to the company

Show the interviewer that you know and admire their company’s practices. Be sure to research the company and industry ahead of time. Learn about its data science approach, and discuss why you could adapt to it easily. For example, you might mention that you respect the company’s use of innovative technology or the way it communicates with clients.

Is Data Science a Rewarding Career?

When considering the switch to a new career field, people often want to know if it’s worth it to put in the extra time and effort studying, honing new skills, and preparing for interviews. Fortunately, the answer for many is a resounding yes!

Glassdoor labeled “data science” as the third most desired career in America, with a median data scientist salary of $108,000. The Bureau of Labor Statistics (BLS) lists the median salary for all US workers at $49,800, meaning data science salaries are over double the national average.

In 2019, LinkedIn ranked data science as the top most promising job in the US and reported a 56% increase in job openings.

According to a recent survey, COVID-19 has not slowed data science opportunities either: 50% of analytics and data science organizations have suffered no impacts (42.1%) or have actually grown in size (7.6%) during the pandemic.

The demand for data science links back to more companies ramping up big data, Internet of Things (IoT), and cybersecurity efforts and small-to-medium-sized enterprises also expanding their data analytics capabilities.

Is Data Science Hard?

Data science is definitely intellectually rigorous and can have a steep learning curve. It may be coined by Harvard Business Review as the sexiest job of the 21st century but it’s not all glamorous—there is a lot of time spent cleaning the data, importing large datasets, building databases, and maintaining dashboards. To thrive as a data scientist, you should enjoy quantitative fields and be passionate about helping companies make more data-driven decisions.

According to LinkedIn, SQL is the most commonly requested skill in data science jobs, with Hadoop and Spark also rising in popularity. You will probably need to learn a programming language like R, SAS, or Python; brush up on mathematics with a focus on statistics and probability, linear algebra, and multivariate calculus; and learn some data visualization tools like Tableau.

It is recommended to learn coding from scratch, as even a parameter change can disrupt results and there’s little margin for error. As you progress as a data scientist, you may specialize in machine learning algorithms, deep learning, and natural language processing, among other related fields that deal with big data and unstructured data.

Successful data scientists should also be well-rounded with soft skills, like interpersonal skills, communication skills, teamwork, and storytelling. Often, these skills cannot be taught in a textbook and only be developed on the job by collaborating with stakeholders across the business, product, and tech teams.

Each step of the data science process can be challenging. First, companies need to acquire the right data from different internal and external sources (e.g. credit card transactions, weather data, order history, competitor intelligence) and make sure it is structured in a legible format.

Once the data is queryable, you then have to build complex models and algorithms to extract meaningful insights and convey them in a way that answers key business questions and influences stakeholders.

Although data science can require advanced domain knowledge and many job qualifications do mention post-graduate education, you don’t need a master’s degree to break in as an entry-level data scientist. There are many online courses, workshops, bootcamps, and books available, so it is definitely possible to acquire technical skills on your own, especially if you come from a software engineering background or hold a bachelor’s degree in another STEM field.

Those interested in data manipulation and computation may also want to explore the work of data analysts, data architects, business intelligence analysts, machine learning engineers, and similar job titles when searching for opportunities.

What’s Next After Data Science?

Experts believe Data Science to be the most future-looking skillset given the increased usage of data analytics and machine learning to make more informed business decisions and run their businesses. It has largely helped organizations to obtain meaningful insights from unstructured and raw data. 

Data scientists’ jobs mainly require them to help the organization make smart investment decisions, target the right consumers, assess associated risks, and contribute toward capital allocations.   

After developing your data science skills and gaining years of experience, you can explore different domains like marketing, sales, data quality, finance, business intelligence, etc., and even serve as a consultant with leading data-driven firms.

What are Some Popular Data Science Job Roles

Some of the prominent data science roles are listed below.

Data Scientist

A Data Scientist’s primary job role is to extract consumable information from structured and unstructured data with computer programming tools and processes. Their job also includes creating methodology and blueprint to present information to stakeholders. They are also supposed to maintain databases.

Data Analyst

A Data Analyst has the responsibility of analyzing the data, identifying trends, and creating a predictive model based on data studied. Another critical responsibility of a Data Analyst is to translate findings into reports, which can be understood by the management, and help them accurately visualize the possible outcome. They are also supposed to maintain databases and data systems.

Data Engineer

Data Engineers are required to study data, develop data set processes, prepare the predictive model, and build algorithms through which stakeholders can easily consume raw data. It may include developing dashboards and reports that can be accessed and used by all stakeholders. Data Engineers need to have strong communication skills to be able to understand client’s requirements and objectives.

Data Mining Engineer

The job of a Data Mining Engineer is mainly extracting data from an extensive database and analyzing them. They are also responsible for building and maintaining software and digital infrastructure to study big chunks of data. 

Data Architect

Data Architect’s role is to ensure that data used in creating a blueprint of a project is stable, secure, and available to all stakeholders at all times. The job role includes collating, organizing, centralizing, maintaining, and protecting a company or client’s data.

Data Statistician

This job role includes critical responsibilities such as extraction of data using statistical methodologies and analyzing, organizing, and contextualizing data and its subsets. A Data Statistician is supposed to conduct tests to determine the reliability and accuracy of data.

Project Manager

Data mining, extraction, testing, analysis, and application for creating a blueprint is a wide field of work that requires management to optimize the resources being used on a project. A Project Manager’s role is to oversee and guide the execution of the project. They act as a medium between the team and clients to communicate requirements and changes in the project.

What are the Key Data Science Concepts?

Artificial Intelligence

Artificial intelligence is based on simulating human intelligence processes through algorithms. In other words, it is the discipline that tries to create systems capable of learning and reasoning like a human, learn from experience, find out how to solve problems under given conditions, contrast information and carry out logical tasks.

Business Intelligence

Business intelligence is about the ability to transform data into information, and information into knowledge, so that the decision-making process in business can be optimized. From a more pragmatic point of view and associating this concept directly with information technologies, we can define Business Intelligence as the set of methodologies, applications and technologies that allow the gathering, purifying and transforming data from transactional systems and unstructured information into structured information. The information can then be used to support decision-making with respect to business.

Big Data

Big data refers to the sheer volume of data, both structured and unstructured, that floods businesses of all kinds every day. The massive generation of data from social networks, mobile devices, sensors and other data sources created challenges that motivated the creation of novel tools and techniques.

Big Data are all data sets or combinations of data sets whose size (volume), complex (variability) and growth rate (speed) impede the capture, management, processing or analysis using conventional technologies and tools such as databases relational and conventional statistics.

Data Mining

Data mining is a set of techniques and technologies that allow exploring large databases, automatically or semi-automatically, finding repetitive patterns that explain the behavior of these data. These patterns can be found using statistics or search algorithms close to Artificial Intelligence and neural networks. The intention of data mining is to provide valuable information to companies to help them make future decisions.

Machine Learning

Machine learning is a scientific discipline in the field of Artificial Intelligence that creates systems that learn automatically. Machine learning refers to the process by which computers develop pattern recognition or the ability to continually learn and make predictions based on data, after which they make adjustments without being specifically programmed to do so.

Machine learning automates the analytical modeling process and allows machines to adapt to new situations independently. 

Deep Learning

Deep learning is a subset of machine learning and an aspect of artificial intelligence that deals with emulating the learning approach that humans use to obtain certain types of knowledge. In its simplest form, deep learning can be seen as a way to automate predictive analytics. The algorithms that deep learning uses are stacked in a hierarchy of increasing complexity and abstraction.

Text Mining

Text mining seeks to extract useful and important information from heterogeneous document formats and large data collections, such as web pages, emails, social media, magazine articles, etc. This is done by identifying patterns within texts, such as word usage trends, syntactic structure, etc. It adopts machine learning techniques for pattern recognition and understanding of the new information collected.

Data Analytics

Data analysis in an approach that involves data analysis, specifically Big Data, to draw conclusions. By using data analytics, companies can be better equipped to make strategic decisions and increase their turnover. Its main objectives are to improve operational efficiency, improve and optimize the UX and customer experience, and refine the business model.

Statistics

Inescapable! Also powerful, sometimes counterintuitive. Statistical methods are traditionally used for descriptive purposes, to organize and summarize numerical data. The main function of statistics is the collection and grouping of data to build statistical reports, always from a quantitative point of view.  

Data Manipulation

Data manipulation is defined as the process of taking disorganized or incomplete raw data and standardizing it so that you can easily access, consolidate and analyze it. It also involves mapping data fields from source to destination.

Data manipulation aids the usability of the data by transforming it to make it compatible with the end system, as complex and intricate data sets can hamper data analysis and business processes. For data to be used for end processes, it must be transformed and organized according to the requirements of the target system.

An example of data manipulation could point to a field, row, or column in a dataset and implement an action such as join, parse, clean, consolidate, or filter to produce the required result.

Data Cleaning

Almost all data sets include some outliers that can skew the results of the analysis. You will need to clean the data for optimal results. The data is thoroughly cleaned for further analysis. You will need to change null values, remove duplicates and special characters, and standardize the format to improve data consistency.

Data Warehouse

A data warehouse is a system used to generate reports and for data analysis. They consist of a central data repository integrated by one or more sources and store current and historical data, which are analyzed and then used to generate reports.

Is Data Scientist a Stressful Job?

Several data professionals have defined data analytics as a stressful career. So, if you are someone planning on taking up data analytics and science as a career, it is high time that you rethink and make an informed decision.

Below, we have listed the top reasons why you should avoid choosing a data analyst career so that you can effectively make strategic career choices.  

The concept of learning throughout your career

Data analytics can be an extremely broad term. Data means different for different companies, hence, professionals will need to learn specific skill sets, every time they join a new company. Some companies desire machine learning knowledge, whereas, others may focus more on programming languages.

And since data analytics is a multidisciplinary job, you will be expected to possess a broad variety of skills including business understanding and technical skills, which might not always be possible.   

Your degree is not enough

Acquiring a bachelor’s and a master’s degree in the required field might not be enough to get you a headstart in your career. You need to understand the intimidation that comes from command-line interfaces, working with big databases, and such other tasks. Only then you will be able to effectively counter the type of problems that data professionals face on a daily basis, in real-time.   

The task of constantly negotiating, communicating and educating business stakeholders

Not only should you possess the knowledge of handling data in a way that business profits, but you should also be able to negotiate efficiently. You have to sell your ideas for new data analyst initiatives to business stakeholders, and also push them for additional resources, like new technologies, more labor, more storage, and so on.   

You have to constantly be resourceful

When it comes to the data science and analytics industry, it is not quite popular for handing out favors. At the end of the day, it is the job of the data professionals to get the job done, be resourceful and find the answers to their problems, themselves. Data professionals are responsible for scheduling meetings with relevant stakeholders and learning more about the business problems, the desired outcomes, and forming strategies accordingly.   

Executing the most difficult tasks on your own

Working on tasks like building machine learning models might seem fun, but data analytics is not just that! A massive portion of your time will be spent on understanding the idea, understanding where the data is coming from, and ensuring it is authentic. And if the data is not satisfactory, you will have to build a data pipeline to extract the desired data.   

The requirement of possessing meaningful industry connections

Data analytics is not a domain where advanced skills can get your career to new highs. If you are someone looking for ways to build a career in this industry, you need to constantly keep in touch with industry professionals. Attend hackathons and meetings, establish your LinkedIn and Twitter connections, and do not limit yourself to your current company. The more valuable connections you have, the more established your profile will be in front of your employers.   

You have to be crazy enough to get unexpected results all the time

Crazy ideas are the only best ideas. And as a data analyst or a scientist, you need to have plenty of those. You will be responsible for yielding unexpected and crazy solutions, and this requires being on an extraordinary level, always. You have to keep the spark of awesomeness, always enlightened.     

Your employer will be business-centric

Your employers might not always be tech-savvy professionals, and might not possess much knowledge about data analysis and big data science. And this might become a problem since your team might be given a task that requires months of work, but from a business point of view, this might not be a favorable choice. But, this is where you can show off your people skills and explain why it would be ideal to take up space while working on that particular issue. 

Textbook knowledge won’t work

As mentioned above, data analytics might not be the career that you can successfully accomplish with the help of degrees and textbook knowledge. You have to get your hands dirty and learn about the practical skills that can actually get you your dream job or position. Even for beginners, it would be excellent if they could work on volunteer projects or several internships and enhance their knowledge regarding industry-based knowledge.  

What Motivates you to Become a Data Analyst?

Data analysts love solving problems 

Data analytics is a fast-paced, challenging career centered on problem-solving and thinking outside of the box.

As a data analyst, you’ll work with a number of different teams who require your skills and knowledge to provide them with insights into how they can improve their processes.

Data analysts come from many different backgrounds

While many data analysts come from a more analytical or technical background, we have found that anyone can become a data analyst if they apply themselves. For example, we have many successful graduates who used to work in marketing, sales, teaching, customer service, finance, architecture, HR, and IT.

Data analysts are in high demand

Employers struggle to find qualified data analysts, and the demand keeps growing. The average junior data analyst salary in the United States is $51,349 per year, while senior data analysts can earn as much as $78,204.

Data analysts are constantly evolving

Data analysis moves quickly, and data analysts are constantly learning and advancing in their careers.

There is practically no limit to how much you can improve your skills and progress in your career as a data analyst.  

Data analysts can work in many types of companies

As a data analyst, you have the opportunity to work for startups, agencies, large corporates, or even freelance. Your skills can be utilized by all kinds of businesses who want to understand their processes, customers and business more. 

Data analysts are shaping the future

Almost all companies are collecting data on their customers, and correctly knowing how to interpret such data is becoming of increasing importance.

Data analysts define how a business is currently operating. It’s up to them to look for changes, identify patterns, and spot anomalies that give an indication of how a company or organization is performing. 

Is Data Science the Future?

Experts have said that 80% or more of a data scientist’s job is getting data ready for analysis. Now, technology providers sell platforms that automate tasks and abstract data into low-code or no-code environments, potentially eliminating much of the work currently done by data scientists.

Read Also: Data Monetization

“[The data scientist title] will probably fade into the background because more tools are becoming prevalent,” said Kathleen Featheringham, director of AI strategy and training at management and IT technology consulting firm Booz Allen Hamilton. “To me, it’s like website design years ago when you had to have people who really like code, but now you can go online and use a tool that will build your website for you.”

Will AI and Automation Replace Data Scientists?

Predicting the future of artificial intelligence requires understanding its past. The earliest realm of data science — analytics or stochastics — incorporated probability theory and analysis into programming. The R language emerged as an open-source equivalent of SASS and SRS, two ancient analytics packages that trace their lineage back to Fortran. Python’s incorporation of similar packages made it the go-to language for combining the results of such data analysis with other components.

These gave way to visual pipeline tools such as Alteryx or Microsoft BI, which reduced the need for programming experience, yet required enough understanding of statistics to know what these packages were doing. It is unlikely that the need for competency in modeling such pipelines will ever fully go away, so while the notion of being a dedicated data scientist will fade, the need for subject-matter-expert analysts will continue.

On the other hand, the argument can be made that the field of machine learning engineering, which requires an understanding of higher-level mathematics, is already moving outside the realm of the data scientist. This falls into the realm of adaptive cognitive science, where programmatic neurons handle tasks such as speech generation, image recognition, contextual classification and similar areas.

Finally, graph cognition, which uses mathematical graphs to support inferential analysis, was outside the realm of “formal” data science for some time but is now being drawn back into the machine learning engineer role because pure machine learning solutions tend to be inadequate for building inferential systems.

One area that is becoming especially intriguing today is neural networks as graphs, while the emergence of Bayesian and Markov blankets within graph systems offers an entirely novel way to manage predictive analytics.

As is typical of careers within the technology space, the data scientist as a distinct entity is fading away, but emerging careers demonstrating the advancement of programming into these areas are as important as ever.

Summary

Working with data is challenging, and requires constant learning. We need to keep studying and tracking what is new, besides learning about the businesses we are working with.

Even so, working with data is rewarding, and is cool to know that your work is positively affecting the business.

We hope the information presented in this will help you make the right decision when it comes to building a career in data science.