Step by Step Guide To Become A Data Scientist

Step by Step Guide To Become A Data Scientist Data Science is a multidisciplinary approach that converts ordinary data into meaningful information, in layperson terms. It uses mathematics, statistics, analytics, and programming to uncover trends and form business insights for various organizations, helping them make data-driven decisions.

Data Science is one of the fastest growing fields in the domain of artificial intelligence. Through this short learnpath, you will be able to navigate your way through the skills needed to enter, and grow, within the realm of Data Science. It will give you a brief overview of the field, its important and the relevant skills you need. For more details, view our extensive learnpath link given at the end.

The Importance of Data Science Today, most industries are powered by data, and they all need individuals who can help manage, collate, and interpret the same. The opportunities in the field are endless with sectors such as Finance, Healthcare and IT hiring data scientists.

What learning paths can you take?

This depends on the course you want to pursue to achieve your goal of becoming a Data Scientist. There are two alternative paths you can take: a. E-learning path: This would mean taking on online certification courses or opting for free crash courses that will equip you with relevant skills. b. Traditional path: The offline approach is perfect for people looking to gain a master’s or bachelor’s degree in Data Science and who want the physical experience of attending college.

We would suggest going for an online course or a series of online classes since the advantages of the same outweigh the disadvantages. When it comes to Data Science, the majority of the work you will do will be on a desktop or laptop. Hence, an online course is more convenient, cheaper, and less time-consuming and equips you with the same skills as an offline course.

Moreover, you have an option of pursuing a bachelor’s or master’s degree online too.

Skills required 1. Basic skills

●

Curiosity and novel thinking: Due to the dynamic nature of this field, you may also have to solve problems you have never seen before or come up with a novel solution. The ability to think outside the box is important.

●

Analytical skills: To help organizations find data-driven solutions, it is important to know how to critically analyze and review information to get the most out of it.

●

Storytelling and communication skills: Storytelling and communicating through data means presenting your data in a format, with a beginning and ending, using concrete results that you have obtained from data processing.

●

Mathematics and statistics: Mathematics and statistics lay the foundation for Data Science and have many uses and applications within the field. Some important topics include Calculus and Econometrics.

How to Learn?

Here is a list of courses that can help you learn these topics: 1. Statistics for Data Science Provider: Henry Harvin, Price: ₹6750 or $87, Duration: 8 Hours, Level: Intermediate 2. Statistics Essentials for Analytics, Edureka Provider: Edureka, Price: ₹5,999 or $77, Duration: Flexible, Level: Intermediate ●

Data Visualization: There are many tools in the market today that can help with data presentation and visualization, making the insights easy to read and understand.

How to Learn?

However, here is a list of courses that can help you learn these topics:

1. SQL Essentials Training & Certification Provider: Edureka, Price: ₹7999 or $103, Duration: Flexible, Level: Beginner 2. SQL Developer Course Provider: Henry Harvin, Price: ₹6750 or $97 Duration: 16 hours, Level: Intermediate

2. Intermediate skills

●

Advanced Excel: It is an excellent tool for data visualization and basic data analysis. Advanced Excel functions can be used for data exploration and even automating tasks.

●

R Programming: It is an open-source, high-level programming language used for statistical computing and is excellent for exploratory analysis, containing many libraries and data analytics tools.

How to Learn?

R plays a very important role in Data Science and you will benefit from the following courses to learn it: Data Analytics using R, Henry Harvin Provider: Henry Harvin, Price: ₹13,500 or $185, Duration: 8 hours, Level: Intermediate

●

Python: Python has gained momentum recently and is one of the most widely used data science programming languages in the world today, a basis for ML and even AI. How to learn?

This is a topic you can learn through online videos or various certification courses. Here are a few Python courses that are highly recommended.

1. Learn Basics of Python Provider: Simpliv, Price: ₹15,190 or $208, Duration: 3.3 hours, Level: Intermediate 2. Introduction To Python Provider: Datacamp, Price: Subscription, Duration: 4 hours

●

Introduction to Machine Learning and Deep Learning: This includes knowing what ML and DL are and understanding their applicability.

How to Learn?

These topics can be learned through various online such as free videos on youtube etc. and offline resources such as research papers, books and other publications. One of the courses that can help you in this learning process is: IBM Machine Learning Professional Link:Provider: Coursera Price: Free, Duration: 6 Months Level: Intermediate

●

Introduction to Artificial Intelligence: Knowing the basics of AI and Cloud Computing can broaden your knowledge base in this field and help you holistically develop your skills.

3. Advanced skills ● Data Exploration: This is done through EDA (Exploratory Data Analysis), which helps investigate and analyze datasets to find the main characteristics and discover patterns and anomalies.

How to Learn?

These are topics you can learn through online videos or certification courses.

Here are some of the courses that can help you learn these topics: Data Exploration and Visualization using Excel/Python Provider: Fore School of Management, Price: ₹8,000 or $103, Duration: 3 Days ●

Time Series Data: Develop an understanding of time series data and tools to analyze and forecast time series data, as it is an important part of data science.

How to Learn?

This is a topic you can learn through online videos or various certification courses. Here are a few courses that will teach you time series analysis and forecasting:

1. Practical Time Series Analysis, Coursera Provider: Coursera, Price: Free, Duration: 26 Hours, Level: Intermediate 2. Time Series Analysis in R, Great Learning Provider: Great Learning, Price: Free, Duration: 5 Hours, Level: Intermediate ●

Supervised learning using R or Python: Supervised learning algorithms are essential to Machine Learning,hence, it is important to learn it in either R or Python and understand why and where it is used.

How to Learn?

This is a topic you can learn through online videos or certification courses. Here are a few hand-picked courses that will teach supervised learning in R: Data Science with R, Henry Harvin Provider: Henry Harvin, Price: ₹13,500 or $185, Duration: 24 Hours, Level: Intermediate ●

Unsupervised learning using R or Python: It is used for three main tasks, namely Clustering, Association and Dimensionality Reduction.

How to Learn?

This is a topic you can learn through online videos or certification courses. Here is a course that will teach you unsupervised learning.

Unsupervised Machine Learning in Python, DataCamp Provider: Datacamp, Price: Subscription, Duration: 4 Hours, Level: Intermediate

●

NLP: NLP refers to Natural Language Processing using text as a container. One should develop an in-depth understanding of processes such as Stemming or Lemmatisation, which are based on NLP. How to Learn?

This is a topic you can learn through online videos or certification courses. Here are some of the courses that can help you learn it: NLP Certification Training with Python, Edureka Provider: Edureka, Price: ₹17,795 or $244, Duration: 6 Days, Level: Intermediate

Insights 1. Model-building: Models can be built and validated through R or Python, and free practice datasets can be downloaded from organizations like the World Bank, Amazon Web Services, and Google datasets. This real-world application of the knowledge gained helps one grow in a practical field such as this.

2. Upskill: This is a fast-paced industry, hence, it is important to constantly upskill and hone your skills. This includes the following: ●

Attending Hackathons

●

Finding internships where you will gain practical experience

●

Networking with industry experts

●

Use platforms like Kaggle, LinkedIn, and Reddit forums to connect with like-minded people.

3. Practice: Whether you are learning the skills on your own or are enrolled in a course, you must constantly practice what is being taught in order to deepen your knowledge and master the nuances.

Conclusion Learn more about data science, it’s importance and the pros and cons of each learning path you take.

While this learnpath briefly covered all the relevant skills, if you wish to go into details and learn more about each skill at the beginner, intermediate or advanced level, alongside recommended courses to make your learning easier, explore our extensive Data Science Learnpath by Careervira, spread across three levels: Beginner to Novice, Novice to Intermediate and Intermediate to Expert.

We, at Careervira, offer comprehensive learnpaths on a plethora of topics and domains to help you advance your career. You can explore them through our advice page.

Careervira.com is a global edtech market platform. With over 12K+ courses from 65+ global partners across tech and non-tech domains, Careervira.com provides 70+ key decision-making inputs to search and compare from amongst top global courses to zero down on your requirements by learning partner, skills, course, price, and learn type.