Both data scientists and data analysts deal with a sea of data. They solve critical business problems and arrive at logical conclusions through big data analysis. Collecting, storing and maintaining data is necessary to churn out such insights from structured, semi-structured and unstructured data. You can consider shifting to a data science role for reasons like mastery, impact, relevance, pay and plenty of perks. The data science platform market size is expected to grow from $95.3 billion in 2021 to $322.9 billion in 2026, at a CAGR of 27.7%. So, this could be a great time to enter this rapidly growing industry.
Analysing past data helps determine future goals and take an organisation to new heights. The common skills for both data scientists and analysts include maths, statistics, mining, warehousing and visualisation. The professionals also use Python and R, which are currently the top programming languages. But how to make the transition from data analyst to data scientist? Read on to find out.
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Enrol in a course
Consider the Data Science Architect Program offered by GeekLurn. The syllabus consists of an Introduction to Data Science, Statistics and Probability, Data Manipulation, Supervised Learning, Dimensionality Reduction, Reinforcement Learning, Sequences and File Operation and NumPy, Pandas and Matplotlib. You can be a part of tech talks, data science meetups and access more than 40 powerful tools and resources.
It also offers 320+ hours of live interactive sessions with eminent data scientists and real-time training sessions with 1:1 mentorship. You will gain tech-enabled, job-relevant skills and become eligible for the role of a data scientist.
Know What a Data Scientist Does
A data analyst interprets, cleans and then uses the information to create reports that can aid in business decisions. A data scientist, however, performs the following with a novel perspective and approach:
Data Scientist Roles
|Building models based on unique business needsSyncing information data and deep diving into it.Creating predictive models using machine learning algorithms, like decision trees and gradient boosting.||Evaluating models to validate the analysis’ accuracy. Building automation tools and techniques, like libraries for the data scientist team. Taking part in the ETL pipelines.|
Having an idea of what is expected of you can help you understand the new skills that you will need to learn, if any.
Make a Skill List
A few must-have skills for a data scientist are API interaction, machine learning models (neural networks and regression), handling data visualisation and cloud computing tools, and knowing distributed computing frameworks, such as Hadoop. Wrangling, computing and processing large data sets are also essentials. Check which skills you might need to strengthen and then apply for a course that can help you master them thoroughly.
Master Deep Learning
A data analyst can become a data scientist if they get an idea of deep learning, along with machine learning, which come together to decode image and text data. This is necessary since traditional ML algorithms only work with tabular data forms. Deep learning is a more significant part of the data science ecosystem than data analysis. Learning here is:
Know how to perform classification tasks directly from sound, image and text. Make sure to achieve accuracy through the models that may outdo human-level performance at times.
Know Model Building Skills
Data scientists create algorithms from scratch. So, you must gain a detailed understanding of calculus and linear algebra, along with advanced maths. All of these will help you figure out how ML algorithms behave and work. Improving precision and accuracy can give you an idea of the behind-the-scenes calculations and how to apply them in business problems. These are necessary for the model-building process, since it involves setting up ways of data collection to find statistical, mathematical and simulation models.
Improve Your Coding Skills To Shift From Data Analyst To Data Scientist
A data analyst is usually involved in identifying trends from business data. This is done through linear regression. So, you may not have to perform coding on a regular basis. But one of the main tasks of a data scientist is to derive information from the massive data sets using programming languages. It is a good idea to master the art of coding to be able to play with data. You must also understand the syntax of programming languages like Java, which is critical for both entry and exit level roles of a data scientist. This can also help in the smooth transformation from a data analyst to data scientist. Data analysts may take up a few real-time projects with the help of GeekLurn’s program to understand coding skills and strengths.A data analyst can become a data scientist if they dedicatedly follow these tips. Try to deploy your newly-learned modules in small projects. Get them reviewed by your mentor to be able to sharpen your understanding and reduce the chances of major errors. Lastly, always stay updated with the latest data science trends, since it is an ever-evolving field.