The data science roadmap explained
- Data science is a scientific process where a professional called a data scientist analyses the data and uses it to bring useful insights.
- The field is becoming a terrific career with much room for advancement. Because of the great demand, competitive pay, and extensive benefits, being a data scientist has been called “the most promising career”.
- There is a tonne of information on the market regarding data science that is expanding rapidly worldwide, but with the right roadmap, anyone can ace their career in it.
Given the enormous volumes of data being produced today, data science is a crucial component of many sectors and is an aggressively contested topic in IT. Data science has shown its value in terms of business operation optimisation. Businesses are leveraging it positively to expand their operations and improve consumer happiness.
Data scientists are in high demand in India, which has led to an upward career trajectory and attractive salaries from employers. By 2026, India’s big data business, which is currently worth USD 6.9 billion, is expected to account for 32% of the global market and USD 20 billion. In this article, you will learn about data science and how to become a data scientist. Here we discuss the data science learning path.
What is data science?
Data science is an online scientific process in which a professional, a data scientist, analyses the data and uses it to derive useful insights. Data scientists build artificial intelligence (AI) systems capable of performing tasks that frequently require human intelligence by applying machine learning algorithms to various data types, including numbers, texts, photos, videos, and audios.
Analysts and executives can then transform the insights into real commercial value. Various data science projects for beginners can be tried to get a good idea of your suitability for the field.
Data scientists are in super demand in all enterprises for using Big Data as an insight-generating engine. Organisations increasingly rely on data scientists’ abilities to stay one step ahead of the competition. It plays a critical role in sustaining and expanding businesses. Whether it’s to enhance customer support and retention, streamline product development, or mine data to uncover new business prospects. Additionally, this article will examine technical and non-technical data scientist skills and help you determine an ideal path for your career.
READ MORE: What is Data Science?
Why is data science important?
According to IDC, the next five years, the digital data produced will be more than two times the digital data since digital storage was made possible. IDC predicts that by 2025, 175 zettabytes of data will be generated globally. With the help of data science, businesses are able to effectively comprehend massive amounts of data from various sources and obtain insightful information for better decisions.
Key skills in data science
As the demand for the field increases, acquiring the required skill set is fruitful for gaining a competitive edge. The following are the skill sets or data science topics required to be mastered in order to ace your career in data science.
- Any programming language like – R/ Python
- Data Extraction Transformation and Loading
- Data Wrangling and Data Exploration
- Machine Learning Algorithms
- Advanced Machine Learning (Deep Learning)
- Big Data Processing Frameworks
- Data Visualisation
Data science roadmap
There is a lot of information available in the market about the increasingly growing field in the world, but the access to information always develops a fog in the students’ minds about how to pursue this option as a career choice. This confuses many students and makes it tough when data science is easy to learn, given that the right path is followed. Don’t worry because here, we give you a clear data science roadmap.
- Learn key mathematics concepts
Learning key mathematics concepts thoroughly can ease your process of learning data science and help you in building a successful career in it in the longer run. The foundation of learning data science are four key ideas—statistics, linear algebra, probability, and calculus. Calculus aids in model learning and optimisation, even if statistical ideas are the foundation of every model. Probability helps forecast future events, and linear algebra is especially used to extract useful information with large datasets.
- Fine-tune statistics
A more complex and practical branch of statistics that aids in organising experiments for testing hypotheses challenges us to fully comprehend the relevance of metrics and, furthermore, aids in measuring the value of the outcomes.
- Master in probability
The core tenet of data science is the conceptual and practical applications of probability. Despite the size of the area. The simplicity of probability principles for preparing an efficient data science learning programme. It might save you a lot of time while searching for the right curriculum. A data science learner intends to build an efficient tutorial on probability for data science using very simple language.
- Become proficient in programming
After you have mastered the fundamentals of mathematics, you should learn how to programme to translate your mathematical acumen into scalable computer programmes. Python and R are the two most widely used programming languages in data science, so that’s a good place to start.
Anyone can learn to programme in them as they are open-source languages. Linux, Windows, and macOS support them both. Additionally, they both are user-friendly and have simple syntax and libraries.
- Advance in data analytics
For extracting data from and interacting with sizable databases, SQL is employed. Understanding the many kinds of normalisation, creating nested queries, utilising co-related questions, executing join operations, etc., on the data, and extracting in raw format should be prioritised. This data will be further cleansed, possibly using Python libraries or Microsoft Excel.
One should have a basic understanding of creating tables, adding data, updating information, deleting data, and running simple queries in SQL. This way, you need to learn the data analytics roadmap.
- Dive into Machine Learning
The fundamental competency needed to become a data scientist is machine learning. Large corporations utilise machine learning to create different predictive models, categorisation models, etc., and to optimise their planning in accordance with the forecasts
- Learn deep learning
On the other hand, deep learning is a more sophisticated form of machine learning that uses training data to create neural networks. This framework incorporates several machine learning algorithms for performing different problems. Recurrent neural networks (RNN) and convolutional neural networks (CNN) are two examples of neural networks.
- Grasp Natural Language Processing (NLP)
In order to make sense of the data and apply it in several different applications, NLP use has become more and more crucial as daily text data production rises. Most likely, you have already been utilising some of the most potent NLP tools without even being aware.
NLP is used in the background to run Google Translator, a free multilingual machine translation service. Your questions will be correctly understood and addressed by Amazon Alexa or Google Assistant using speech recognition that runs on NLP.
- Get the hang of data visualisation tools
These days, hiring a data scientist or Android developer depends in part on the tools and methods they employ. Using these technologies can help firms get business insights and maintain an edge over the competition.
- Familiarise with deployment processes
Once you have a group of effective models, you can operationalise them so that other programmes can use them. Predictions are made either in real-time or in batches, depending on the business requirements. Models are exposed via an open API interface for deployment.
Is data science a good career option?
Data science is a fantastic career with a ton of potential for future growth. A data scientist has been dubbed “the most promising career” because of the high demand, competitive salary, and abundance of benefits.
Big data salaries are very expensive across the nation as companies compete for the greatest pool of personnel with data expertise. The average compensation for data scientists with between one and ten years of experience in India is INR 11,30,000 per year. It is in the INR 5,700.00 to INR 19,300.00 range.
How can I become a data science professional?
In most cases, formal education is necessary to become a data scientist. Here are some ideas for the next actions.
Obtain a degree in data science
Although it’s not always necessary, employers typically prefer to see proof of your academic accomplishments to make sure you have the skills to handle a data science position. To gain a head start in the industry, try studying data science, statistics, or computer science for your related bachelor’s degree introduction to data science course.
The curriculum aims to educate you for analytical and leadership roles in various sectors by fusing big data analytics, machine learning, and statistics in an ideal way. Learn how to effectively work in teams, apply machine learning to predictive modelling, solve real-world problems, generate strategic and tactical suggestions, and manage processes, work, and people.
Pursue a postgraduate data science programme, like courses such as M.Sc. in data science.
A data science Bootcamp is a rigorous and all-encompassing curriculum created to prepare you for a career in the industry. Programmes may just last a few months, but they will prepare you for some top jobs.
|Cost||INR 1.5 lakhs to INR 3.5 lakhs||INR 3 lakhs to INR 6 lakhs|
|Duration||A few weeks to a few months||3 years – 4 years|
|Commitment||Part-time or online||Part-time or online|
Develop pertinent skills
Consider enrolling in an online course or a suitable bootcamp if you feel you could improve your hard data skills. The following are some of the abilities you should possess.
- Python language
Python, its most commonly used and flexible programming language in the data science sector today, can manage all tasks, from data mining to building websites to operating integrated equipment, in a single, unified language.
- SQL data sources
Designed for organising and accessing data stored in a relational database management system, SQL is a domain-specific programming language. SQL can be used to update/add new data as well as read and retrieve data from databases. A SQL query is frequently written as the initial step in any evaluation process.
- AI and machine learning
The few data scientists who are actually skilled in machine learning stand out. Machine learning can automate a sizable portion of a data scientist’s job, like cleaning process removes repetitions, and helps analyse vast amounts of data using algorithms and data-driven models.
Master your skills with Online Manipal
If you’re interested in a rewarding career in the fields of data science and artificial intelligence, the online data science programme offered by Manipal Academy of Higher Education (MAHE) through Online Manipal is a fantastic place to start. The professionals who want to advance in their careers are the target audience for this Master of Science degree. An M.Sc. in Data Science will equip you to launch a successful Data Science career with the technical know-how.
A data science course taught by professionals in the area can be a huge advantage whether you wish to work in the sector or switch to another profession. You’ll discover how to hone your analytical skills, problem-solving strategies, and programming language proficiency. Visit Online Manipal now to learn more about data science programmes.
Information related to companies and external organizations is based on secondary research or the opinion of individual authors and must not be interpreted as the official information shared by the concerned organization.
Additionally, information like fee, eligibility, scholarships, finance options etc. on offerings and programs listed on Online Manipal may change as per the discretion of respective universities so please refer to the respective program page for latest information. Any information provided in blogs is not binding and cannot be taken as final.
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