Becoming a Data Scientist, Data Analyst, Financial Analyst and Research Analyst

Original article was published on Becoming Human: Artificial Intelligence Magazine

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Though some may even opt for the position of a data analyst, it’s still second in the race as the most preferred position for many aspirants is still the post of a data scientist.

The data scientist is called the sexiest job of the 21st century. If you are planning to enter the field of data science, chances are that your aim is to become a data scientist as it’s the most coveted post these days. Though some may even opt for the position of a data analyst, it’s still second in the race as the most preferred position for many aspirants is still the post of a data scientist. If playing with data and finding hidden insights where others don’t see it or find it is something that you love and want to take up as your career, you may even consider being a financial analyst, or a research analyst.

Though you will come across several career choices that let you stay close to data and figures, the one that wins hands down is a data scientist.

But it shouldn’t mean that just because everyone else is aiming for this post, you too should join the bandwagon. You will need to understand what the job entails, the kind of skills and aptitude you will need, the pay package you will get, the chances of furthering your career that you will have, etc. before taking your final pick.

Let’s delve deeper to take a look at the different positions that you may consider in order to arrive at a well-informed decision.

1- Becoming a data scientist

Whether you are a student or a professional looking to shift careers, positioning yourself for a data science career could be a smart move. While students can pick up degree courses (which include programs in data science and analytics) run by several universities, professionals may pick up short-term courses conducted by reputed institutes or organizations. They may even take up bootcamps if they are ready to slog it out and don’t mind the intense learning sessions where a lot of information is packed in every session.

It’s important to note here that though a majority of data scientists have backgrounds as statisticians or data analysts, you will also find others coming from non-technical fields such as economics or business. So, just because you aren’t proficient in coding and programming or don’t have an IT background shouldn’t stop you from pursuing a career in data science.

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If you are wondering how professionals from diverse fields like economics, mathematics, statistics, business, IT, etc. end up in the field of data science and make it work in their favor, you should look closer to find that they all have one thing in common: an ability to solve problems and communicate the well along with an unquenchable curiosity about how things work and even look for problems that others might not have even though of.

Apart from the qualities mentioned above, you’ll also need a rock-solid understanding of the fowling to become a data scientist:

  • Statistics and mathematics
  • AI and machine learning
  • Coding languages like Python and R along with Java, C/C+, or Perl
  • Data visualization and reporting technologies
  • Databases such as MySQL and Postgres
  • Hadoop, Apache Spark, and MapReduce

Additionally, you should be able to able to work with unstructured data, which are undefined content that refuse to fit into database tables. Some examples of unstructured data include blog posts, videos, video feeds, audio, social media posts, customer reviews, etc. Since such data include heavy texts that are grouped together, sorting such data that isn’t streamlined is an extremely tough task. No wonder why unstructured data is often called ‘dark analytics’ due to its complexity.

As a data scientist, it’s mandatory for you to have the skills necessary for understanding and manipulating unstructured data gathered from diverse platforms because this way, you will be able to unravel insights, which can prove to be helpful for informed decision making.

1.1- Typical job duties

The role of a data scientist doesn’t come with a definitive job description. Here are a few things that are you are likely to handle as a data scientist:

  • Collecting a huge quantity of unstructured data, which is then transformed into a more usable format
  • Working with a wide range of programming languages such as Python, R etc.
  • Leveraging data-driven techniques to solve business-related problems
  • Having a rock-solid grasp of statistics, including both Bayesian statistics and classical statistics
  • Staying abreast of analytical techniques like deep learning, machine learning, and text analytics
  • Collaborating with both IT and business cells and communicating the findings in lucid language to the stakeholders or clients
  • Looking for patterns and order in data in addition to spotting trends that can help the bottom-line of a business

Data Scientist is better than Financial Analyst, Data Analyst and Research Analyst

1.2- Things to consider before accepting the job offer for the post of a data scientist

Before you accept a data scientist position, there are a few things about the organization that you should assess:

  • Does it give data its due value? You should remember that a company’s culture has a significant impact on whether it should employ a data scientist. So, apart from checking how much it values data, you should also see if it has an environment that supports analytics and if it already has data analysts involved. If not, you may be burdened with the job of a data analyst too, which could become tiring and cumbersome soon.
  • Does it handle huge amounts of data and have complex issues that it wants to get solved? The organizations that really require data scientists have two factors in common: They deal with massive amounts of data and face complex issues on a daily basis that needs to be solved. You will find these typically in industries such as pharma, finance, etc.
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  • Is it willing to bring changes? As a data scientist, you would expect that your recommendations are taken seriously and acted upon. You invest a lot of effort and time to find ways, which will let the organization/company you are employed with to function better and improve its bottom-line. In response, the organization/company should be ready and willing to follow through with the results/recommendations of your findings. If not, working in such an environment where your hard work doesn’t come to fruition would be futile.

For some organizations/companies, employing a data scientist to guide data-driven business decisions based could be a leap of faith. So, before you accept the position of a data scientist, make sure the organization/company you are going to be working for has the right attitude and is prepared to make some changes if needed.

Data Science Curriculum

2- Becoming a data analyst

To become a data analyst, you should have a degree in either of these fields

  • Computer Science
  • Economics
  • Statistics
  • Mathematics

Additionally, you should have the following qualities:

  • Ability to analyze huge datasets
  • Experience in data reporting packages and models
  • Ability to write all-inclusive reports
  • An inclination for problem-solving and an analytical bend of mind
  • Attention to detail
  • Strong written and verbal communication skills

As a data analyst, your job would include the following (though it won’t be limited to these):

  • Gathering and interpreting data
  • Spotting trends and patterns in data sets
  • Defining new procedures for data collection and analysis
  • Collaborating with business/management teams to ascertain business needs
  • Evaluating the results
  • Reporting these results back to the appropriate stakeholders or other members of the business

2.1- Things to consider before accepting the job offer for the post of a data analyst

Similar to the case of a data scientist, your potential employer should have a conducive work environment and be willing to accept and act upon your findings. At the same time, it shouldn’t confuse the role of a data analyst with that of a data scientist. If it does, taking up the position would mean working on aspects that you aren’t trained in, which would soon start creating problems. Even if it doesn’t, it will overburden you for sure.

3- Becoming a financial analyst

You will need to have a bachelor’s degree — preferably with a major in finance, economics, or statistics, to become a financial analyst. MBA graduates with specialization in finance too can enter the field as senior financial analysts.

Apart from educational qualifications, you should be proficient in problem-solving, have strong quantitative skills, and be adept in the use of logic along with having good communication skills. Your duties will include crunching data and reporting your findings to your superiors in a concise, clear, and persuasive manner.

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4- Becoming a research analyst

To work as a research analyst, you are likely to need a master’s degree in finance or have CFA (Chartered Financial Analyst) certification in addition to some others licenses or certifications that the job may need depending on which filed you are going to be employed in. you should also have the following skills and personality traits:

  • Attention to detail
  • Be extremely organized and reliable
  • Good with numbers
  • An inquisitive, logical bend of mind
  • Ability to refine huge amounts of information into particular takeaways

Perhaps you can now see why being a data scientist is the top pick among all these positions.

Becoming a Data Scientist, Data Analyst, Financial Analyst and Research Analyst – Magnimind Academy

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Becoming a Data Scientist, Data Analyst, Financial Analyst and Research Analyst was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.