How to become a data scientist: 6 steps to start a data science career
Big tech companies like Facebook, Google, and Amazon have been investing heavily in data science to improve their services. This has led startups across the globe to hire data scientists as well. Data science is a diverse field that includes machine learning, statistics, and data visualization.
Data scientists are in high demand for the job market because of their skills in machine science, statistics, and data visualization. There is a massive shortage of high-quality skills and some data scientists get paid in high six-figure incomes in US Dollars. As per Forbes, the salaries and job opportunities for data scientists continue to increase. So let’s dig deep into how you can start a data science career?
What is the best way to get into data science?
There are a few ways to become a data scientist. The best way would be to get into it from the inside by being promoted internally.
The popular path to becoming a data scientist is to start as a data analyst and work your way up. Data scientists need good mathematical skills, an understanding of statistics, and the ability to think critically. Contrary to popular perception, a good data scientist has the best business skills in addition to knowing data science very well. It’s like they know what they are looking for.
There are many other ways to get into data science, but one of the easiest and most effective is by becoming a data scientist in your current role. Experienced data scientists can move into Director of Data Science and similar director- and executive-level roles.
Who is a Data Scientist?
A data scientist is someone who extracts and interprets data. Data scientists work in big data, machine learning, or AI companies. Data analysts, data engineers, business intelligence specialists, and architects are the various occupations of a data scientist.
What does a Data Scientist do?
Simply put, data scientists extract meaning from data. The process of becoming a data scientist involves taking on the role of many different positions. From creating data visualizations to learning how to code and write software, a data scientist may be asked to do a variety of tasks. Data scientist cycles through phases of data collection, preparation, analysis, and testing. Data doesn’t always work as anticipated, in which case the model is revised or scrapped.
Why there’s a demand for Data Scientists?
Data is being generated day by day at a massive rate. Data scientists extract valuable data insights from these data sets and use them for various business strategies, models, plans. Big Firms, companies are hunting for good data scientists to extract valuable insights to power their growth.
Characteristics of a Successful Data Scientist
Data scientists are analytical and creative. Data scientists are meticulous as they review large amounts of data. They seek out patterns and answers through an analytical lens. Professionals in the data science field must know how to communicate in multiple modes. There may be a lot of dead ends and wrong turns, but data scientists should possess patience. Successful data scientists have a strong technical background, but the best data scientists also have great intuition about their data.
Multiple Known Paths into Data Science
Employers will want to see some academic credentials. Alternatively, you have to provide evidence of your ability to work with data and quantitative analysis. A portfolio is a great approach which we cover later in this article. The typical paths are:
- A related bachelor’s degree can help in studying data science, statistics, and computer science.
- A master’s degree is a great way to go into data science research.
- Joining an Online Program/Bootcamp
What are the qualifications needed to be a top data scientist?
The qualifications needed to be a top data scientist are relatively simple. A candidate must have an undergraduate degree in computer science, mathematics, statistics or engineering, and experience in programming. Data analytics is important for businesses, and there are many ways to become a data scientist.
If you don’t have the degree for being a data scientist, consider getting some additional qualifications to fill the gap and catch up with several others who have the required qualifications. Some of the non-education qualifications needed to be a top data scientist are knowledge of statistics, computer programming skills, and data science experience.
If you have both the education and the qualifications then you are in a good place. If you have one you still have some chances but if you have none then you have got some work ahead of you.
List of skills required to become a data scientist
In order to become a data scientist, it is important to master multiple disciplines. It is also necessary for a data scientist to have extensive knowledge of statistics, computer programming skills, and data science experience.
A data scientist is a person who uses all the following skillsets to analyze and handle large amounts of data:
- R programming/Python Programming
- Data extraction
- Data wrangling
- Machine learning algorithms
- Advanced machine learning/Deep learning
- Big Data Frameworks
- Hadoop platform
- SQL database
- Programming skills
- Business Acumen
- Communication Skills
Is it necessary to have experience in related fields and work experience to become a Data Scientist?
The short answer is no. You don’t need experience in related fields and work experience to become a Data Scientist. But it’s highly recommended that you have a background in statistics or math. While it’s not necessary to have experience in related fields and work experience. If you’re a math major or have had prior data science-related jobs, you may be more likely to get a job in the field. Data scientists are in demand across industries and there is a general shortage of the right skill set.
Do note that some companies prefer either experienced data scientists – when they have to work on complex projects – or look at hiring people from a certain background, such as specific schools. Do your research before applying to roles. Consider strengthening your profile before you apply. You can run a free strengths comparison at Sukiru with a Data Scientist working in your target company.
How can I become a Data Scientist on my own?
If you want to take the first steps yourself or are wondering how to become a data scientist for free? Then follow our six-step guide. This guide is also good for people looking at how to become a data scientist from scratch?
STEP 1: Select a programming language (Python or R).
Python is the most preferred coding language for Data Scientists and has overtaken R, but there is a market for R skills as well. Python supports Numpy, Pandas, MatplotLib, Seaborn, Scipy, etc. It’s highly recommended that you start with Python before going any further. Several post-graduate courses now have Python listed as a pre-requisite or a pre-course before the data science curriculum begins.
STEP 2: Statistics
Having knowledge of statistics is essential for becoming a Data Scientist. Statistics helps data scientists to learn about patterns in data by drawing histograms, performing hypothesis tests, getting p-values, and learning about non-parametric data transformations.
STEP 3: Learn SQL
SQL is a foundational skill that will help you get started in data science and can also be applied to other fields like business analytics. SQL is a programming language that is used to create and maintain databases. SQL can be used by data scientists to interact with the database as well as to perform various other tasks. SQL knowledge includes creating tables, inserting data, updating data, deleting data, and performing some basic query operations.
STEP 4: Data Cleaning
As a data scientist, you will be in charge of cleaning and organizing the data set. This will include deleting duplicate entries, removing rows with missing values or other data not relevant to the analysis. A clean data set is extremely important to the success of the project and while this step may sound trivial it is the foundation of data science.
STEP 5: Exploratory Data Analysis
The fifth step in the process of becoming a data scientist is Exploratory Data Analysis. This helps you understand what patterns exist and how to interpret them. The process helps you to identify any outliers and helps to determine how many data points are needed for your analysis.
STEP 6: Machine Learning Algorithms
Machine learning algorithms are the most crucial step for data scientists. They allow them to make decisions based on data. The algorithms can be used to make predictions or to identify the most relevant pieces of information in a dataset. This allows data scientists to take their careers to the next level.
How many years does it take to become a data scientist?
Do you keep wondering how long does it take to become a data scientist? You are at the right place. We present the steps on how to become a data scientist in less than one year.
Learning the basics of data
A degree in a STEM field is the best way to become a data scientist, but if you don’t have access to one or it’s too expensive, you can still start a career in data science. You’ll need to learn the basics of data science and get your hands dirty with some coding.
Completing a data science course is ideal for learning fundamentals like how to collect and store data, analyze, model, and visualize data.
Gain the required skills to become a data scientist
The skills needed to become a data scientist include programming, statistics, and computer science. Evidence of the requisite skills is important in addition to having the right credentials. Review the list of skills required to become a data scientist in this document.
Review data scientist certifications, post-graduate education, courses, and bootcamps
In order to be a data scientist, candidates must first become certified. Not all certifications are equal and some require a post-graduate degree. A data scientist must be able to understand the end-to-end analytics process and have expertise in various tools. Often, education is a good way to fast-track your entry into a career and provide a credible signal to prospective employers.
Learn to work in a data job
A data scientist is someone who uses data to make decisions. Data scientists require prior experience of working with data. If you are just starting your career then consider getting to work in a data role before taking the plunge into data science directly. You may choose to also build your skills and complete a certification while you are working.
Everyone starts somewhere, even if it’s not impressive. Interesting questions and datasets are key to projects. Projects allow you to go through the life-cycle of data science. Find data, ask questions, then code. Some examples of projects are health tracking, predicting football game-winners, and running sentiment analysis on Twitter.
Build a portfolio
In order to land your dream job, you must work on a portfolio. Your portfolio is a good way to show your skills. Accompany data with narrative to make it interesting and easy for prospective employers to understand. Don’t include the whole body of work in specific job applications. Highlight skills from the whole process of data science in your resume and cover letter. Check out these sample portfolios for you to get inspired about showcasing your skills to the world.
- Portfolio by Donne Martin
- Portfolio by Claudia Ten Hoope
- Portfolio by Yan Holtz
- Portfolio by Hannah Yan Han
Push yourself beyond your limits
Lead or senior data scientists want to hire someone who can save them money or make customers happier. You need to keep searching for new questions and answer harder ones in order to learn more. Progress should be reflected by work, not just thoughts. Some ways to push your boundaries are: Try working with a larger dataset than you’re comfortable with, start a project that requires knowledge you don’t have.
Remember No Pain, No Gain
Wondering how hard is it to become a data scientist? It is a bit hard but you will reap the dividends of your efforts for a long time to come
Learn from Others, Build in Public
Learning from others is a good way to improve your knowledge. Online communities are good to engage with other data scientists. Some good online communities are Quora, Kaggle. In order to learn, you need to be able to build in public and share your work with others. If you don’t have a mentor, find a community to join. Here are some ways you can start building your network and share what you’ve learned:
– Attend meetups
– Attend conferences
– Create a blog
Develop soft skills
In order to become a data science, it is important to have the right skill set. This includes having strong skills in statistics and programming, as well as soft skills like public speaking and storytelling. The first step is to develop soft skills, such as how to present findings effectively and convince stakeholders of the value of your project. Remember it’s not a role to work in a silo to simply follow instructions. A data scientist has a high level of autonomy and should be comfortable working in ambiguous environments.
Data Scientist Specializations and Career Paths
Data analysts spend most of their days analyzing data and writing recommendations. They work with existing data and provide a summary of sorts that details the company’s performance. They help a data scientist with their work.
Data Engineer is a combination of a software engineer and a data scientist. Data engineers have a key role to ensure the availability of data by setting up data pipes and are responsible for the structure of the data in the right manner. They work very closely with data scientists. Several times data engineers progress to being data scientists and the past experience is helpful to them.
Data science is all about solving business problems through data. As a data scientist, you’ll need to communicate with other data scientists. Critical thinking is important when evaluating data or finding insights in large datasets. Being able to think critically requires different angles and perspectives on problems.
Growing into a Data Scientist Role from a Data Analyst Role
What’s the difference between a Data Analyst and a Data Scientist?
Data scientists and data analysts have similar roles. Data scientists are more senior than data analysts. Data scientists often have to form their own questions of the data while data analysts might support teams with set goals in mind.
Transition into a data science role
Data scientist jobs are not easy to transition into and transitioning in the same company is more likely than transitioning from another company. For trying to switch outside your company consider a formal qualification in data science. It is not easy to train new employees and it takes a lot of time to create value. However, when they have the necessary skillset, this is the way to go.
Consider a data science specialization
Consider gaining a specialization to make your entry into the field faster. There are multiple career paths that people take. Sometimes being a data engineer gets your foot in the door at the right company and then transitioning into a data science role is easier.
All the best to the start of your career in Data Science.
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