Career In Data Science As Fresher: How To Start?

Data Science has emerged as a very promising career path. In this article, we have tried to help freshers start their career in data science with some useful tips.

Data science is a highly competitive and rewarding field. Getting a job as a data scientist isn’t easy and you need to be very persistent to succeed in this field. One does not become a data scientist overnight. It takes a lot of learning, experience, and understanding of the concepts, especially if you want to start a career in data science as a fresher.

• Introduction
• What Does A Data Scientist Do?
• Learn the Basics of Data Science
• Build Your Online Portfolio
• Create a GitHub Profile
• Update your knowledge, pick up a relevant course
• Networking with Data Scientist Communities
• Improve Your Business Skills
• Create an Impressive Resume

Introduction

Before you get started to become a data scientist, there are some questions that you need to ask yourself:

Do you love numbers, figures, and graphs? Data science is all about numbers and figures. If you don’t like playing with numbers, you will find it frustrating later in your career.

Can you program without difficulties? Data scientists should be comfortable with programming languages like R, Python, etc.

Are you willing to learn and start at an entry level before reaching a proper data scientist role?

When you have a positive answer to the above questions, you can go ahead and start acquiring the skills associated with data science. Let me share some infallible tips to help you make a career in data science as a fresher or how to get data science job as a fresher.

Must Read – What is Data Science?

What Does A Data Scientist Do?

As a data scientist, you should have impeccable analytical skills, software management, appropriate communication strategies, and new measurement theories and applications, with the aim to –

• Perform data analysis with maximum reliability
• Anticipate difficulties that may be encountered in the process
• Select advanced data science tools to meet these challenges
• Present the observations obtained from the data

Learn the Basics of Data Science

It is imperative that a data scientist master the fundamentals of data science. Data science comprises many disciplines including statistics and math, computer science, data analysis, data handling, artificial intelligence, machine learning, deep learning, and others. To start a career in data science, you should know of –

• Math
• Statistics
• Programming Languages
• Analyze and Manipulate Data
• Machine Learning
• Data Visualization

Math for Data Science

Knowledge of linear algebra,  probability, and calculus is a must for a data scientist. The challenge is sometimes not knowing the math behind the analysis but rather interpreting the results that drive the further course of action. Master the below topics – 

Linear Algebra: Vector Spaces, Eigenvalues & Eigenvectors, Singular Value Decomposition, Orthogonalization & Orthonormalization, Symmetric Matrices, Matrix Operations, etc.

Probability: Random Variables, Random Experiment, Conditional Probability, Probability Distributions

Calculus: Integration, Differentiation, Partial Derivatives, Vector-Values Functions, Directional Derivatives, Gradient, Hessian, Jacobian, Laplacian, and Lagrangian Distribution, etc.

Statistics for Data Science

For everyone willing to start a career in data science as a fresher, it is essential to know statistical calculations. Statistics is an essential concept for producing high-quality models, such as how grammar is used to build sentences. They are the basis of data science. You should be familiar with the concepts of Measures of Central Tendency, Measures of Dispersion, Standard Deviation, Statistical Tests, Descriptive Statistics, Samples or Inferential Statistics, etc.

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Programming Languages 

Data scientists do need proficiency in at least one programming language. The most widely used is Python, followed by JavaScript, Java, R, C/C++, SQL, MATLAB, Scala, etc. Python is generally preferred because it is an open-source programming language with many inbuilt libraries like Matplotlib, NumPy, Pandas, Scikit-Learn, TensorFlow, Keras, PyTorch, Theano, etc., facilitating data science tasks. 

Related – Pytorch vs Tensorflow – What’s the Difference?

While you don’t have to be the best programmer in the world, as a data scientist, you should know how to use them.

Data Analysis

A data scientist should know how to manipulate and analyze data and present the findings based on data. Knowledge of tools like Tableau, Looker, Google Data Studio, SQL, MS Excel, and even Python is necessary. 

Machine Learning

Machine learning has become a must-have skill for data scientists. It creates predictive models, drawing on past data to predict future trends. Different Machine Learning algorithms like linear and logistic regression, decision trees, SVM, Naive Bayes, etc., are used to solve various real-life data problems. A data scientist needs to know how these algorithms work, how to choose and use machine learning models according to the problems to be addressed, how to configure hyperparameters and reduce the error rate of the models, etc.

Data Visualization

Understand the basics of good data visualization and reporting. You don’t need to become a good graphic designer, but you need to know how to create data reports to present to a layman. You should have hands-on knowledge of data visualization and reporting tools like Tableau, QlikView, Microsoft Power BI, Datawrapper, Google Charts, Grafana, and Chartist. js, FusionCharts, Datawrapper, Infogram, Plotly, MS Excel, Zoho analytics, etc.

Build Your Online Portfolio

Employers won’t pay you to do something you’ve never done before. If you are currently working, try to apply everything you are learning in your work. On the contrary, if you are not working, start developing your projects that involve all the new data science tools you know.

You should have around five projects relevant to the job role of a data scientist, such as data cleaning projects that include data preparation, data, munging, data cleaning, data storytelling, visualization project, group projects, etc.

Start displaying the value your projects added to your workplace. If you are not employed anywhere, showcase your projects and their impact on a personal blog, GitHub, a video on YouTube, Twitter, or any other digital medium that helps you to make your skills go viral.  

Create a GitHub Profile

Set up a GitHub profile. Your profile will highlight your data science skills most practically. So an impressive GitHub profile with some good projects in Jupyter Notebook or R Markdown format will certainly work for you and may impress your hiring manager. Make sure you have clear problem statements, clean code files, and follow a particular style to create projects. You should – 

• Keep the data in the same folder as your notebook
• Mention the information of any external libraries and packages you are using
• Include a README file in Markdown format
• Create a gist of every project to help the user get an overview of the project

Update your knowledge, pick up a relevant course

Never stop learning. Even if you find your first job as a data scientist, never stop upskilling yourself. Read blogs and academic articles to improve your technical skills and your innovative capacity. Don’t forget to always keep a balance in your professional skills. Data science courses can help you in sharpening your existing skills.

Networking with Data Scientist Communities

Data science communities can be a good stepping stone for freshers. You can discover new ideas, showcase your projects, learn from experts, and find new job opportunities. You should join some data scientist community pages and keep visiting them. Some helpful data scientist communities are Kaggle, Reddit, IBM Data Science Community, Open Data Science, Data Science Central, Stack Exchange, etc.

Improve Your Business Skills 

Communication skills – Data scientists should have good communication skills to translate their technical findings to other non-technical teams or stakeholders. This way, you can help non-technical teams like marketing and sales to understand your findings, with the help of which they can make informed business decisions.

Business understanding – Data scientists should understand the business well enough to find the information from the gathered data. Also, their data wrangling and interpretation activities should be aligned with an organization’s business goals.

Create an Impressive Resume

You need to create a winning resume to start your career in data science as a fresher. Most employers use Applicant Tracking Systems (ATS) to shortlist the best candidates. Ensure that your resume has  –

• The right keywords
• Link to your GitHub and professional profiles
• Mention your data science projects
• Any previous related work experience

You can explore multiple online resume creation tools with impressive templates. Do use them and embark on your journey on the data science path.

Useful Resources

To gain more perspective about data science and to stay updated with the relevant news, these online resources can be of great help –

• Thinkful 
• KDnuggets 
• Facebook Research
• Data science 101
• Flowingdata
• Kaggle
• Data Science Central
• Inside Big data

Concluding Remarks –  

Considering its highly dynamic nature, keeping a tab on the new developments in data science is vital. Every aspect of data science jobs is subjected to growth and evolution. 

Programming languages, software, tools, and technologies that shape data science will continually change and become more robust. To stay more market-relevant and have a fruitful career in data science, keep up with the market dynamics.

If you want to learn machine learning, click here for articles on different topics.

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