How to Build a Career in Data Science?

How to Build a Career in Data Science?

Data-driven jobs are growing extensively in the modern digital world which has encouraged youths to skill up in domains such as logistics, business intelligence, statistics, machine learning, and data architecture. Data science is one such career that has witnessed visible growth over the past few years for those with skills in coding, analytics, mathematics, statistics, and data visualization. Building a career in data science is lucrative as it is in high demand across organizations of all industries worldwide. Thus, becoming a data scientist can revolutionize your career. However, learning the right data science skills takes time and effort, especially for those who are new to data science. This blog is to guide you to build a successful data science career in Nepal.

What is Data Science?

Data Science refers to data mining using programming skills, statistical preparation, and visualization techniques. It revolves around the ability and willingness to turn business issues into answers with the stored data. Data Science is basically the intersection of software development and machine learning. Data professionals analyze data and create algorithms to define the data patterns to improve the performance of organizations. Data Science offers versatility with great job opportunities as it allows organizations to maximize efficiency, and reach business goals.

Who is a Data Scientist?

Data Scientists are trend and pattern finders who analyze and interpret complex data. They decipher large volumes of data and analyze them to find trends and patterns in the data to get a deeper insight into the data. This allows companies to take important decisions toward their business goals. Data scientists are bridges between the business and IT worlds as they analyze complex datasets to extract insights that organizations can leverage into actions. Businesses depend on the insights extracted by data scientists to develop a plan for implementing decisions.

Data scientists use impeccable analytical skills and appropriate communication strategies to:

  • Perform data analysis with maximum reliability.
  • Anticipate difficulties encountered in the process.
  • Select advanced data science tools to improve performance.
  • Present the observations obtained from the data.

How to Start a Career in Data Science?

With the continuous growth of information technology, the demand for data science jobs is increasing rapidly. Although freshers are unlikely to demonstrate immense expertise and work experience in data science, if strengthen a few important things, one who is new to data science can even start a career in data science in Nepal. One can follow the following things to build a successful career in data science.

  • Learn the Fundamentals of Data Science

It is imperative for you to know the basic fundamentals of data science to become a data scientist. Data science comprises mathematics, statistics, computer science, data analysis, data handling, machine learning, artificial intelligence, and others. You must understand these 6 basic fundamentals to start a career in data science:

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


  • Math

A data scientist must have the knowledge of linear algebra,  probability, and calculus - it becomes challenging to interpret the results driven by the data if you don’t know math. The topics below are important to learn to build a career in data science 

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

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

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

  • Statistics

For those who want to start a career in data science from the scratch, the knowledge of statistical calculations is a must. It is an essential concept for the production of high-quality models - one should understand the concepts of Measures of Central Tendency, Measures of Dispersion, Statistical Tests, Descriptive Statistics, Standard Deviation, and Samples or Inferential Statistics.

  • Programming Languages

You must have proficiency in at least one programming language as a data scientist in Nepal. Python is the most common programming language among data scientists in Nepal. Other programming language options to start a career in data science are JavaScript, Java, C/C++, R, SQL, MATLAB, and Scala. Python is preferred mostly because it is an open-source programming language with inbuilt libraries such as Matplotlib, NumPy, Scikit-Learn, Pandas, TensorFlow, PyTorch, Keras, Theano, and more, which are essential to facilitate data science tasks.

  • Data Analysis

As a data scientist, you must know how to analyze and manipulate data. Once you have your findings on your plate, you should be able to present them to the audience or the responsible person live. You must learn a few data analysis tools to build a career in data science. Knowledge of tools like MS Excel, Tableau, Looker, Google Data Studio, and SQL, is essential for a data scientist.

  • Machine Learning

Machine learning is a must-have skill to start a career in data science as it is essential to create predictive models, drawing on data to predict trends and patterns. Machine Learning algorithms such as linear and logistic regression, SVM, Naive Bayes, decision trees, and more are used in determining and solving real-life data problems. As a data scientist, you must know how these algorithms work, how to configure hyperparameters, and which machine learning models should be used to solve the problems and reduce the error rate of the models.

  • Data Visualization

Understanding the basics of data visualization is essential to start with data science. You should not be a good graphic designer but know how to create data reports and present them to the audience. Having a good knowledge of data visualization and reporting tools such as Tableau, QlikView, Microsoft Power BI, MS Excel, Google Charts, Datawrapper, Grafana, Chartist. js, FusionCharts, Infogram, Plotly, Zoho analytics, etc can help you with data visualization.

  • Take a Data Science Course in Nepal

Even though you have a fine knowledge of data science, never stop learning and upskill yourself. Keep reading academic articles and watching data science videos to improve your technical skills and innovative capacity. It helps you keep a balance in your professional skills. You can even join a data science course in Nepal to stay on top of the latest trends in data science. Data science training in Nepal helps you expand your knowledge and enhance your learning experience base in the domain, ultimately sharpening your existing data science skills. You can enroll in a data science program in Nepal at TechAxis, a leading IT training institute in Nepal.

  • Build Your Data Science Job Portfolio

Employers often won’t pay attention to you if you have knowledge but have not done the work before. Develop your own projects that involve data science tools which will be helpful for you to showcase yourself as a data scientist to employers. Build at least five projects relevant to the data science job and put them in your data science job portfolio. Your data science projects may include data cleaning projects that involve data preparation, data cleaning, data munging, data storytelling, and visualization project.

  • Improve Your Business Skills

There is no point in having data science skills but not having any business skills. You will need both business understanding and communication skills to land the best data science job in Nepal.

Business Understanding: As a data scientist, you must have a good understanding of the business as it helps you find the right information from the data collected. If you have a fine understanding of the business, your data wrangling and interpretation activities will align with the business goals of the organization.

Communication skills: Good communication skills are also important for data scientists as it helps them translate and present their technical findings to non-technical teams and the audience. Good communication skills help the marketing and sales teams to understand your findings so that they can make the best business decisions possible.

  • Create an Impressive Resume

A winning resume is a key to starting a career in data science at the entry level. Read over the job description of the data science job vacancy in Nepal clearly and make sure you don’t miss any information. Resumes and data science job applications are commonly eliminated when employers find you didn’t take the time to read the job description. Draw on the details in the job description and craft your resume accordingly and show the recruiter how you are the perfect candidate for the data science job. You must consider putting the keywords as per the job requirements as it significantly increases the chances of you being called for the interview.

Data Science Career Opportunities

Companies around the world are adopting data analysis for business growth which is why Data Scientists are the most in-demand professionals in the technology industry. Data science career opportunities are incredibly diverse. As you start a data science career, it opens up multiple career opportunities and job options for you. Below are the most common career opportunities in data science.

  • Data Scientist

Let’s start with the most obvious one. A Data Scientist job is one of the major roles you can get as you build a career in data science. Data scientists extract, analyze, and interpret large amounts of data. They use the data to develop a hypothesis and explore data patterns with respect to the business agenda. Data scientists are supposed to have a statistical knowledge of data analysis tools and programming languages to solve complex problems. They are responsible for using algorithmic approaches, AI (Artificial Intelligence), machine learning, and statistical tools to make the data useful to make big business decisions that will benefit the company in the future.

  • Business Intelligence Analyst

Business Intelligence Analysts identify potential opportunities for improvement, leverage the data, find trends and patterns, and help the business grow. They are primarily responsible for finding potential issues and presenting solutions to help the organization have a more clear understanding of where they stand and what they should do in the future to improve their performance. The Business Intelligence Analyst job purely aims to improve efficiency, and productivity, and drive sales for the business.

  • Data Architect

Data Architect is of the most essential careers in data science. Data Architects are responsible for designing, developing, and maintaining the data management systems of a business. They basically create blueprints for data management systems that are used to centralize, integrate, manage and safeguard data sources. Data Architects are also responsible for meeting the database requirements of a company and building them to keep up with internal and external regulations.

  • Machine Learning Engineer

Machine Learning Engineering is another popular role in data science. Machine learning engineers are primarily responsible for automating data analysis processes. They design and implement machine learning systems, optimize machine learning algorithms, improve existing AI systems and perform machine learning tests to maintain system performance and functioning. Machine Learning Engineers serve as critical communicators among data science team members - they work directly with the data scientists to develop the models for AI systems and help construct them.

  • Data Engineer

Data Engineers are the core of the organization and are considered the backbone of an organization company. They design, build and manage large databases. They are responsible for building data pipelines, enabling correct data flow, and making sure the data reach the relevant departments. A data engineer also has to share the data insights with the organization through data visualization and help it grow the business.


Now that you know how you can build a successful career in data science, you may now be eager to become one. The above-mentioned process may seem a lengthy process to become a data scientist in Nepal. Well, there is an easy way to start a career in data science in Nepal - by simply joining a data science course in Nepal. But where can you find the best institute to learn data science in Nepal? TechAxis could be the best destination for you for data science training in Nepal. Established in 2017, TechAxis is one of the best IT institutes to study data science in Nepal. It has helped hundreds of students start a career in data science since its establishment.