How to become a Data Scientist? Expert tips and strategies for success

13-Apr-2023

 Companies across the globe are in stiff competition to harness and leverage the power of the data. Skilled and expert professionals are highly sought-after with a promising salary package. Why not become a data scientist if you have an educational orientation in one of the core subjects of Data science- Mathematics, Statistics, or Computer Science? Let us dig into the dynamics of building a strong data science career and learn in detail about data science strategies.  

A look into what data scientists do?

To become a data scientist, knowledge of what are data scientists and the things they do is the key driver. Data scientists are professionals skilled at developing and implementing sets of techniques to perform analytical assessments by transforming raw data into valuable insights. To make sense of the large amounts of data generated daily, data scientists employ tools, programming languages, and software that helps them draw inferences and informed decisions. 

A data scientist in general is a professional who leverages his knowledge of statistics and computational methods to extract insights from colossal amounts of data. Data scientists are skilled in numerous fields including mathematics, Statistics, Computer Science, and domain-specific knowledge. Data scientists have the prowess of working with large and complex sets of data in varied forms. They discover trends, and patterns and draw insight through the application of Machine learning algorithms, predictive modeling, and data visualization.

There is an escalating demand now for data science strategies across numerous industries like healthcare, finance, technology, etc. to optimize productivity and efficiency. Choosing to become a data scientist today would mean treading a promising career path. So what are the ideals necessary for becoming a data scientist? Let us learn further in the post. 

Skills Critical for Data Science Strategies

To perform your duty as a data scientist and to lay out data science strategies, all you need is the right skill sets and the acumen to work your way through competently. Let's have a look at the list of critical skills for data science jobs

 Technical Skills

• Statistical analysis and computing
• Machine Learning
• Deep Learning
• Processing Colossal amounts of data 
• Data Visualization
• Data Wrangling
• Mathematics
• Programming
• Statistics
• Big Data

Non-Technical Qualities

• Strong Business Acumen
• Great Communication Skills
• Strong Data Intuition
•  Analytical Mindset
•  Thinking Out-of-the-Box 
• Critical Thinking
• Decision Making

Tips to Become Data Scientist

As learned above a good blend of technical and non-technical skills is required to become data scientist. To make your career plans easier to navigate, let us brief you with a few major tips to become data scientist which will come in handy along your career journey. 

1. Learn the fundamentals

The ideal way to begin is by learning the basics of the core subjects- Mathematics, Statistics, and computer science. Gaining the fundamental knowledge will provide you with a strong grounding for advanced data science concepts. 

2. Develop Programming Skills

One of the crucial skills data scientists must have is the prowess of programming languages. Familiarizing yourself with one or two languages and gaining proficiency in them will take you a long way. R and Python are among the most sought-after languages. 

3. Build a strong foundation in data manipulation

You must learn the techniques of cleaning, transforming, and preparing data for analysis as these skills that come under data manipulation are critical for a data science career. 

4. Focus on machine learning

Your knowledge of machine learning is vital in a Data science career. You must have a strong foundation in Machine learning and learn about the various algorithms and their working mechanism. You may enhance your skills by practicing implementing actions on real-world data. 

5. Build a portfolio

Building a data science portfolio is important to showcase your skills to potential employers. It should reflect the number of projects you have undertaken as it adds up your credentials and skillsets. 

6. Network and Learn from Others

It is ideal to connect with other data scientists as it helps you in learning from numerous people with shared goals and makes you stay updated with the latest trends. Online communities, Conferences, and Meetup events are some of the channels through which you can network. 

7. Practice critical thinking

The potential to utilize critical thinking helps in analyzing data and solving complex problems. You can cultivate this skill through constant practice by asking questions, testing hypotheses, and challenging assumptions. 

8. Stay curious

Data science is an ever-evolving field. Stay curious and keep learning. The best way to do this is by feeding on good data science books, attending events, and pursuing online courses. Data science courses make you learn about the latest developments in the field.

9. Develop communication skills

Data scientists need to communicate complex findings to non-technical stakeholders. Work on developing your communication skills by practicing presenting your work and explaining technical concepts in simple terms.

10. Practice ethical data science

As a data scientist, you have access to sensitive data that can impact people's lives. Always practice ethical data science by ensuring data privacy, accuracy, and fairness.

 Learning the top Data Science Strategies

The data science field is constantly evolving with advanced technologies arriving now and then With that, higher and more advanced skills also get optimized. Let us get into the top data science strategies that significantly make up the discipline. 

1. Define the problem

A data science task begins with defining the problem, addressing the queries requiring solutions, and the objectives the business wants to accomplish. 

2. Collect and prepare data

After defining the problem, the next step is to collect and prepare the data analysis. Data analysis involves the process of cleaning, transforming, and formatting the data to make it suitable for analysis.

3. Explore the data

After preparing the data, to learn the characteristics of the data aggregated, data scientists explore the data through the processes of visualizations, statistics summaries, and correlation analyses. 

4. Choose a modeling technique

Based on the problem and data exploration, you should choose an appropriate modeling technique. This can be regression, classification, clustering, or another technique.

5. Train and test the model

The following step after modeling is training the model on a subset of the data and testing it on another subset to evaluate its performance.

6. Optimize the model

If the model performance is not satisfactory, you should explore ways to optimize it. This can involve feature engineering, hyperparameter tuning, or other techniques.

7. Communicate the results

After you have developed a model with satisfactory performance, you should communicate the results to stakeholders. This can involve creating visualizations, writing reports, or presenting your findings to a non-technical audience.

8. Monitor and update the model

Data science is an iterative process, and models can become outdated over time. It's important to monitor and update the model regularly to ensure it remains accurate and relevant.

9. Collaborate with others

Data science projects often involve collaboration with other data scientists, domain experts, and stakeholders. Collaborating with others can help you get new insights and perspectives on the problem.

10. Remaining Up-to-date with the latest Trends and Technologies

For a constantly evolving field like Data science, professionals have to stay relevant by catching up with the latest trends and technologies. Some of the best techniques include reading research papers, taking online data science courses, and attending events and conferences. 

Know about Data Science Salary 

Having an idea of how much is data science salary may boost your motivation and interest in breaking into the field. Data science is a vast field with diversified roles and positions. The data science salary structure is thus reliant on the positions, the employing organizations, the location of your job, and much experience you have in the field. While those are the defining factors of the data science salary, a brief salary highlight on some of the most popular data science positions are: 

• Data engineer- US$ 93K. 
• Web analyst- US$69K. 
• Research scientist- US$84K. 
• Business intelligence developer- US$83K.
• (Big) Data architect-US$125K.
• Machine learning engineer-US$112K. 
• Director of analytics- US$130K.

(Source: Payscale)

Data Science Salary By experience

The Data Science salary of experienced and seasoned professionals in the US will go up to US$ 197K with an average annual income of US$137k. 

For those in Mid-level careers, the data science salary in the US can be anywhere between US$81K to US$148K with an average annual income of about US$111K.

An entry-level position in the US offers an annual data science salary of US$62K, with an average income of US$86K.

Get ready for an exciting journey with a Data Science career by following these few tips and suggestions. Learn all the data science strategies and become an industry-ready candidate and be employed by top companies and organizations.  

become data scientist data scientist expert tips data science expert strategies data science career