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Can Data Analysts Take Up Data Science? A Comprehensive Guide

The field of data has seen a vast change in the last two decades. The fast-paced technological development and the internet have put the field of data into the limelight. While data analytics has been there for centuries in some form, data science is a field that has evolved in the past few years as an area of major interest. If you are a Data Analyst and Data Science is a field that attracts you, you are already halfway there. We bring you a few tips, to assist you in your smooth transition from data analysis to data science.

Data Analysts and Data Science

An Introduction to Data Analytics and Data Science 

To understand the transition process of Data Analysts to Data Science, we must first under the basic definition of both terms. Although several people  confuse the terms Data Analysts and Data Science to belong to the same category, here are a few general points on the basis on which one can understand the difference, here are a few fundamental differences between both data : 

What is Data Analytics?

Data Analysis is converting raw data into implementable recommendations. The recommendations help companies gain lucidity, and sound knowledge based on past data in the fields of processes and services. These implementable recommendations are converted into actions that are associated with personalizing customer experience, innovating better-accepted products, augmenting productivity, and enhancing manpower proficiency. The different steps involved in Data Analysis are as follows:

  • Define Objectives: Once the objective of the business is identified, past data related to the domain only is collected.
  • Collect Data: Data is collected from different sources which pertain to the defined objective.
  • Data Cleaning: The data has many anomalies and blanks which can affect the final analysis, thus, the data is run through tools that organize the data. 
  • Data Analysis: The next step is to analyze the data for trends and patterns using different analytical tools.
  • Data Interpretation: The trends and patterns help in interpreting the reason for past events.

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What is Data Science?

Data Science is the process of interpreting and analyzing data with a combination of mathematics, statistics, specialized programming, advanced analytics, artificial intelligence, machine learning, and domain expertise. It mimics human intelligence while analyzing data. The data analyzed can be structured and unstructured. The steps that go into Data Science analysis are as follows:

  • Objectives: In data science, the objectives are not pre-defined, but are shaped during the process.
  • Capture: The data is acquired from different sources and in different formats like text, numeric, voice, and videos.
  • Maintain: The data is complex and datasets are big and are received at a faster pace, thus they are stored using data warehousing tools and techniques. The succeeding step in maintaining data is cleaning it and organizing it for further scrutiny.
  • Process: The cleaned data is then organized into clusters and with the help of different modeling techniques organized.
  • Analysis: The analysis technique involves predictive analysis, regression, qualitative analysis, and various other complex methods.
  • Communicate: The most important role of a data scientist here is to present the analysis findings through reports and presentation, summarized as data Visualization tools, to the non-technical members, clients, and board members.

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Differences between Data Analysts and Data Scientists

The transition process of a Data Analyst to Data Science starts with understanding the difference between the roles of a Data Analyst and Data Science advanced tools and techniques. Let’s have a brief look at the differences in the responsibilities:

Data Analyst’s Responsibilities

A Data Analyst has to examine large structured datasets to uncover patterns and trends, that are analyzed, on the basis on which actionable insights are recommended for the business to augment productivity.

The datasets are collected, processed, and converted to structured form to perform statistical analysis.

Data Analysts also have to maintain databases, prepare reports of the analysis, and maintain dashboards

Data Scientist’s Responsibilities

A Data Scientist in a company has to source data from different sources in structured and unstructured form. These datasets are complex, humongous, and received in very short time intervals. 

A Data Scientist has to interpret these complex datasets to create predictive models to arrive at data data-driven insights.

A Data Scientist has to use advanced statistical methods, machine learning, Natural Language Processing(NLP), and Artificial Intelligence to develop advanced Machine Learning Algorithms. He/she has to design experiments.


 What are the common skills of Data Analysts and Data Scientists?

Data Analysts and Data Science have some common factors that help a Data Analyst for a smooth transition to becoming a Data Scientist. These common skills are as follows:

  • Data Mining: Data mining is the process of sorting through datasets to identify trends and patterns, relation between variables, and predicting trends.
  • Data Warehousing: Data warehousing is the process of consolidating data and information sourced from different sources, into one comprehensive database. The data stored is a combination of current and historical.
  • Data Analysis: Data Analysis is predicting insights into the trends and patterns leading to a certain event, that have been analyzed from the trends and patterns from the dataset.
  • Data Visualization: Both Data Analysts and Data Scientists have to present their findings in the form of reports and visual aids to their clients, team members, and board of directors.
  • Soft Skills: Data Visualization requires Data Analysts and Scientists to be excellent orators with clear and lucid presentation skills. This skill subset is important as the presentation of the insights has to be clear and crisp to avoid any misunderstanding, that may lead to confusion and wrong interpretation, which may further lead to irreparable damage to the company.
  • Critical Thinking: For both Data Analysts and Data Scientist, critical thinking and an eye for detail is crucial. They have to identify trends and patterns and consider minute details of anomalies.
  • Statistical Analysis: Both professions require statistical analysis in most processes of data interpretation algorithms and building models.

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Pathways to Transition

The foundation and basic knowledge for Data Analysts and Data Science beginner level are the same. However, there are certain additional skill sets that a Data Analyst has to learn to foray into the world of Data Science. Here is a brief list of both soft skills and technical skills:

  • Programming Language: Data Science requires the knowledge of advanced programming languages like Python, which is required for complex database wrangling and cleaning. It also proves useful for exploratory data analysis. PyTorch and TensorFlow are used for building complex data models.
  • Statistics: The knowledge of advanced statistics for a Data Scientist is imperative as they have to focus on building predictive models. Statistical Methods like regression, time series analysis, and others are used for testing hypotheses and developing experimental designs.
  • Machine Learning: Data Science works on predictive and prescriptive analysis. Machine Learning is an integral part of prescriptive analysis. Machine learning helps to build algorithms and models, while mimicking human intelligence, to create complex algorithms and models that can analyze complex patterns and their relation in data with minimal human intervention.
  • Economic: A qualification in Economics is very beneficial for Data Analysts and Data Science as it helps in learning statistical results and their implications. Machine Learning also requires an understanding of economics.
  • Domain Knowledge: Data Science is fast gaining popularity in several business sectors. The application of data science varies in different domains, thus, knowledge of the domain of interest for Data Analysts and Data Science applications in the specific domains proves helpful for better employment opportunities. 

What Are the Different Activities Connected to Data Analysts and Data Science Experience?

Educational and technical qualifications for Data Analysts and Data Science knowledge are not the only transition steps that aspiring candidates have to take to get better opportunities, other measures can give them additional leverage. This can be achieved by taking the following steps:

  • Course: There are a plethora of renowned institutions that offer a comprehensive course. Enrolling in these courses helps the candidate get access to the latest market trends and industry-accepted qualifications. The course certificate proves to be another helping hand in getting an opening in the world of Data Science. Capstone Projects offered in the course are another great way of learning while handling a project. The other option for Data Analysts and Data Science learning is free courses that are available on the website.
  • Internship: Many companies offer internship opportunities to aspiring freshers. This opportunity helps the candidate to learn while on the job and also helps in building a good portfolio.
  • Research projects: The internet has many options and topics to do a project related to data Science. Projects with Python and Source codes are available on GitHub. This helps in showcasing the expertise of Data Analysts in the field of Data Science.
  • Data Science Competitions: Many websites like Machine Hack, Kaggle, DataHack, Codelab, Driven Data, TopCoder, AI Crowd, and Tianchi Big Data Se Competition conduct competitions in the Data Science domain. Participating in these competitions helps to fraternize Data Analysts and Data Science peers.
  • Networking: Participation in conferences, and research programs, building peer networks, and picking up opportunities to work with experts and leaders in the same domain increases the prospects of continuous learning along with the exchange of ideas which leads to innovation. 
  • Data Science Communities: There are data science communities that have Data Scientists, Engineers, and people interested in the Data Science field as members. Their main goal is to share knowledge and expertise. These are great ways for networking and connecting to experts, along with learning new things. A few well-known communities are Reddit, Kaggle, IBM Data Community, Tableau, Stack Overflow, Open Data Science, Data Science Central, Dataquest, Driven Data, and Data Community DC.

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Tools and Resources for Transition

The technical knowledge of Data Analysts and Data Science technical requirements differ to an extent. Data Analysts can train themselves in the following technical tools which are widely used by Data Scientists currently. These are as follows:

Programming Skills:

A Data Analyst needs to gain knowledge and training in 

  • Python
  • SQL
  • C/C++
  • R
  • Java
  • Julia
  • Scala
  • Javascript
  • Swift
  • GO
  • SAS

Data Visualization:

Unlike Data Analysts who have to deal with clients for the presentation of data analysis, Data Scientists have to deal with their team members and subordinates to convey the findings of the analysis to get them on board with the research. Data Visualization tools available at present are: 

  • Tableau
  • Looker
  • Zoho Analytics
  • Sisense
  • IBM Cognos Analytics
  • Qlik Sense
  • Domo
  • Microsoft Power BI
  • Klipfolio
  • SAP Analytics
  • Cloud
  • Yellowfin
  • Whatagraph

Statistical Tools:

Data Analysts are usually good in Business Intelligence, in addition, they have to familiarize themselves with these statistical tools that are required for the classification of big data:

  • SAS
  • Stata
  • Regression Analysis
  • Minitab
  • Linear Regression
  • Logistic Regression
  • Microsoft Excel
  • Analysis of Variance
  • Knime

Big Data Platforms:

Data Scientists have to handle a variety of complex and huge datasets that are received at a great velocity. Thus, the big data platforms required by data Scientists are:

  • Hadoop
  • My SQL
  • Microsoft SQL
  • Oracle

Cloud Computing:

The knowledge of cloud computing tools is important to deploy big data solutions easily. These are:

  • Natural Language Processing (NLP)
  • Google BigQuery
  • Microsoft Azure
  • Redshift

Risk Analysis:

Risk analysis is an integral responsibility of a Data Scientist and Data Analyst. The tools for risk analysis differ for both domains. Data Scientists use the following for analyzing risk factors:

  • Delphi tech
  • SWIFT Analysis
  • Decision Tree Analysis
  • Bow-Toe Analysis
  • Probability/Consequence Matrix
  • Open Group Fair Model
  • Cyber Risk Quantification

Machine Learning:

Machine learning is the main tool to learn for Data Analysts and Data Science requirements. Here the machines generate the algorithms for data analysis with minimum or no human intervention. These tools are:

  • NumPy
  • Pandas
  • Matplotlib
  • Scikit-learn
  • TensorFlow
  • Pytorch
  • NLTK
  • Jupyter Notebook

Challenges in Transition

There are many areas in which Data Analysts and Data Science roles match. Yet we have tried to pinpoint a few areas, where a Data Analyst working for the first time in the field of Data Science might face obstacles: 

  • Real-Time Data Handling: The big data that is dealt with in Data Science is huge, complex, and is sourced from various sources. It is a mix of structured and unstructured data and may be irrelevant sometimes. Data Analysts work with structured historical data, thus they might face challenges in dealing with real-time data.
  • Current Technology: The field of Data Science is ever-evolving and changing with the introduction of new tools, technologies, and libraries. Thus, a Data Analyst has to ensure keeping abreast of the current technology.
  • Maintaining Balance: A Data Analyst will face initial hiccups while working in a company. The initial challenge would be to put theory into practice, as practical implementation has a lot of changes and variances as compared to the theory part which is taught in classrooms.
  • Data processing: Data Scientists have the primary responsibility of preparing data with many manipulation techniques to develop modelling tools with them. As with experience Data Scientists develop the ability to identify the data required for the end goal, but a budding Data Scientist would have difficulty initially understanding this aspect.
  • Data Security and Compliance: With complex datasets comes the question of data privacy and data security issues. Data Analysts will find it challenging initially as in data analysis the primary source of data is the company. In the Data Science field, the sources are varied.
  • Communication Challenge: Although the role of Data Analysts and Data Scientists in the presentation of the analysis reports is the same, the domains handled by a Data Scientist are more complex and require expert Storytelling abilities. 

Success Stories in Real Life

In all the previous sections we have given a pathway for transition for Data Analysts and Data Science areas where they have to work. This entire briefing would be incomplete without giving examples of real-life Data Analysts, who forayed into the Data Science domain and have made a place in the influential people’s list globally.

Jeff Hammerbacher

Jeff Hammerbacher completed his degree in BA in Mathematics from Harvard University. Later, he started his career as a quantitative analyst at Wall Street and is among the top 10 influential Data Scientists in the world. He worked as the manager for the data analysis team at Facebook. Today, his interest in data science led him to the founding of Cloudera.  

Andriy Burkov

Andriy Burkov holds a BSc in Computer Engineering and Networking. In his 12-year span of working with the data domain, he worked as a Data Analyst with Wanted Technologies. His interest in the domain of Data Science helped in gain the influential position of Director of Data Science at Gartner. He is the author of the book “The Hundred-page Machine Learning Book”. He is counted in the list of top 10 influential Data Scientists globally. 

Aditya Agrawal

Aditya Agrawal started his career as a Data Analyst with ICICI Prudential dealing with customer retention, customer acquisition, underwriting, actuary, fraud, and claim management. His foray into the domain of Data Science has helped him bag the position of Advanced Analytics Practise Lead in Azooba and leads successfully a team of more than 50 data scientists.

Anubhav Srivastav

Anubhav started as an investment analyst. He works as a Data Scientist. He manages the Data Science department at Viacom18, a media giant. He takes the credit for building recommender systems, deep spatial systems, and knowledge graphs.

Dipanjan Sarkar

Dipanjan Sarkar worked as a Data Analyst with Intel and Red Hat. After successfully transitioning to Data Science he now works as a Data Science Lead at Applied Materials India and deals with Natural Language Programme (NLP), deep learning, and leveraging machine learning. He was awarded Google Developer Expert in Machine Learning by Google. He has seven published books on Machine Learning to his credit.


In conclusion, Data Analysts and Data Science both thrive on experiments on datasets. Both professions use data to extract meaningful insights that help shape an organization. The difference between data analysis and data science is the duration of time the analysis runs. On one hand, where Data Analysis deals with historically structured data, the result turnaround time is shorter, but on the other hand in the field of Data Science, the turnaround time is a little longer. A Data Analyst thus, must inculcate the skill of patience to foray into Data Science. 

The path of transition from a Data Analyst to Data Science is easier for those who not only have good analytical skills but also enjoy developing complex models and algorithms to quantify the hidden and unknown and predict events by developing scalable complex models. 

The article has delved into many suggestions to make it easy for a Data Analyst to change. So the earlier one begins, the faster the efforts bear fruits.

FAQs on whether Data Analysts can take up Data Science 

Q. Can A Data Analyst take up Data Science?

Yes, it is easy for a Data Analyst to transition to Data Science, as both fields deal with data and predicting trends and patterns responsible for events. A Data Analyst who has strong analytical abilities, a passion for dealing with complex datasets, and an inclination for statistics and algorithms can easily foray into the domain of Data Science. 

Q. What steps can a Data Analyst follow to shift to Data Science?

A Data Analyst must learn new tools and technologies like advanced programming language, machine learning, cloud computing, big data platforms and tools, advanced data visualization tools, and statistical tools. He/she should enrol in a well-renowned institute to get proper guidance and learn all the new technologies trending in the current business scenario. He/she should join data science communities, for better networking. He/she should pick up data science projects to build a strong portfolio and understand the practical application.

 Q. Which degree is better a data analyst or data science?

Data Analysis and Data Science are both equally reputed degrees. Although Data Science qualification commands a higher remuneration package and better position prospects. Both Data Analysts and Data Scientists are very much in demand in their respective domains.

Geetanjali Pantvaidya is a Post Graduate in MBA Marketing from Army Institue of Management Kolkatta. A Y2k batch pass out , She started her career with Caltiger.com which the country’s first free ISP. She has over 12 years experience in marketing working in the telecom industry, banking , insurance and the education industry. Hailing from an army family background, the love for travelling was deeply rooted in her veins since childhood, thus, her stint as a travel manager with Thomas Cook. She embarked on her journey as a content writer with a travel company.

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