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How to Learn Data Science in Three Months?

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How to Learn Data Science in Three Months?

Are you an aspiring Data Scientist & want to learn Data Science?

We have some really good news for you.

We present this data science tutorial that will help you to learn Data Science in just 3 months.

Data science uses scientific methods, algorithms, and processes to derive insights from both structured and unstructured data. Data science is multidisciplinary in that it employs various methods from science, mathematics, and machine learning to analyze data. According to the author and consultant Geoffery Moore:

“Without Big Data Analytics, companies are blind and deaf, wandering out on the web like a deer on the freeway.”

The term Data Science is often used interchangeably with business analysis and business intelligence since it uses the methodologies of analysis and intelligence to get the required data. So, let’s look at what exactly is data science and why learn data science. 

What Is Data Science & Why Should You Learn Data Science?

Why Should You Learn Data Science

Why Should You Learn Data Science Source – Cherhan

Initially, BI tools would be sufficient to analyze the structured data available then, but now that data is semi-structured and unstructured, BI is not sufficient to analyze this data. This is where Data Science comes in: Data Science analyzes past and current data using various tools and algorithms.

With Data Science, you can process and analyze large amounts of data, making it popular across various industries. Because of this increasing popularity, Harvard Business Review states that Data Science is the hottest jobs of the 21st century.

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Apart from its increasing popularity, here are a few other reasons you need to learn data science:

(a) With data science, you can use your skills in math, statistics, and programming to make sense of large quantities of data.

(b) While there are so many ways for collecting data, there are only a few resources that can help to convert this data into useful analytical points. Data science can accomplish this, but being a recent development, there are not as many data scientists as required, making Data Science a lucrative career now.

(c) Data Science is essential to facilitate better marketing since companies use data to analyze their current marketing campaigns and plan their future marketing strategies.

Top Benefits of Learning Data Science

Benefits of Learning Data Science

Benefits of Learning Data Science Source – Deviant Art

Data Science is popular and making a career in the field has the most benefits for a professional than any other career in the IT.

1. Problem-Solving Skills

Data science is all about solving problems on a much larger scale and those affecting the whole business. Thus, data scientists gain problem-solving skills which help them think analytically and approach problems logically.

2. Increased Demand

Data Science is a relatively new field and has a high demand for skilled data scientists. Data is valuable for every industry, and thus a professional can get a job as a data scientist in any sector.

3. More Job Opportunities in the Future

The demand is on an upward trend, and learning data science will ensure you are never out of a job. Data collection sources are increasing every day, and so is the demand for data scientists.

4. Learn New Skills

Data scientists need a lot of skills to master data analysis and interpretation. During the course of your career, you will learn various skills like leadership, communication and collaboration, and problem-solving skills.

5. Structured Learning

The best benefit of learning data science is that it has a structured learning program, and you can acquire other skills online using resources like blogs, webinars, and by joining data science communities.

What Do You Need to Learn Data Science from Scratch?

If you have a basic idea of statistics and math, you can learn data science easily. If you are interested in pursuing a career in data science, here’s the best way to learn data science from scratch and some resources to help you along the way.

1. Technical Qualifications

Before you can start the data science career path, you need some form of technical qualification, such as a degree in Computer Science, Maths & Statistics, or Engineering. Though a bachelor’s degree is the basic qualification requirement, it is recommended to get a master’s degree in one of these fields to help you further. 

Along with relevant qualification, the second thing you need is formal experience working as a programmer or computer engineer. Having a certificate or diploma in a specialized field will also help.

2. Special Knowledge

Apart from technical education and experience, to become a data scientist, you need a certain set of special skills and knowledge, such as knowledge of programming languages and coding. A Master’s in Data Science is the best way to get this special knowledge. You can also opt for learning courses to acquire these skills.

Some of the common data science skills include:

(a) Knowledge of Python or C#.

(b) Working knowledge of SQL.

(c) Experience working with Hadoop or Machine Learning.

Some of the soft-skills required by data scientists are attention to detail, problem-solving, focus, communication, and organization.

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3. Work Experience

As discussed earlier, having some formal experience will help boost your career as a data scientist. You can get paid work as a programmer in many companies, particularly in finance, manufacturing, and engineering. Taking a data science internship is another helpful option.

You can also enrol in courses with workshops to get a chance to work in specialized projects. Some examples of these special projects are as follows:

Creating interactive data visualizations: This is where you will be able to present data creatively in a unique format.

Machine Learning: liner and logic regression projects are the most helpful for beginners, but there are other high-level projects that you can take up.

Cleaning data: Complex and big data systems need to be cleaned regularly, and for doing so, you need a good knowledge of Python.

4. Practice Projects 

There are various open-source tools available online to help you practice the nuances of data science and developing your project. Databases, data lakes, and distributed storage are the primary aspects of analyzing data, so you can use this to form your project.

As a data scientist, you need to have a thorough understanding of how data is stored in databases and distributes lakes and administrations. You can interact with data scientists online and enter competitions to be able to present your projects to a recruiter.

This video tutorial will give you a thorough understanding of the steps to become a data scientist.

Data Science Curriculum

When it comes to how to learn data science, the first step is understanding the data science curriculum structure. The data science curriculum is broadly divided into five sections: Python/R programming; statistics and linear algebra; Numpy, Pandas, & Matpolib; ML algorithms; production systems. Let’s look at each of these in detail.

1. Python/R Programming

Python is a popular programming language and a basic part of the data science skillset. So, to answer how to learn data science,  thorough knowledge of Python and R programming languages can be very helpful  

You will be able to understand how to run a Python program in different ways and problem-solving skills used by experienced programmers. You can refer to online resources like Python Docs to understand PYthon better once you have the basics down.

Python Programming

Python Programming Source – Extreme Tech

2. Statistics & Linear Algebra

Statistics is the primary way to analyze data and an important part of learning data science. If you have a basic idea of statistics and algebra, brush up on key concepts again, since these subjects are just as useful in machine learning.

3. Numpy, Panda, & Matplotlib

If you have a basic understanding of machine learning and programming, you might have heard of these libraries. Using Numpy, Panda, & Matplotlib is the best way to learn data science, and these libraries can be used for your personal projects.

4. Machine Learning

Machine learning algorithms can be used for analyzing real-world data. If you are a beginner, the UCI ML Repository is the best way to learn data science where you can work with toy data sets. 

Machine Learning

Machine Learning Source -Towards Data Science

5. Production systems

Understanding an organization’s computational systems will help your data analysis efforts. Some of the top systems you need to learn data science include AWS, Google Cloud, and Microsoft Azure.

Learn Data Science in Three months

The role of a data scientist is somewhere between a math software engineer and data communication engineer. A data scientist will formulate strategies for businesses and process data based on those strategies. A data scientist is a senior-most role in any organization.

But before that, you need to know how to learn data science in-depth to be able to establish a career in the field. The following steps will guide to becoming a data scientist in three months.

Month 1: Start with Data Analysis

Month 2: Move on to machine learning

Month 3: Finish off with tools used in real-world by data scientists

1. Data Analysis

Most data scientists start off as a data analyst, so having a complete idea of the data analysis concepts is helpful.

Data analysis involves four subjects or skill areas:

(a) Maths and Statistics

If you have a degree in computers or engineering, your math and statistics will most likely be good, but brush up on your skills nonetheless.

(b) Python Knowledge

Python is the primary programming language used by data scientists, so your next step should be to get a working knowledge of Python. You can do this by joining a training course to learn the language.

(c) Data Wrangling

Data wrangling refers to mapping raw data to some other format that makes it easy to analyze. 

(d) A Basic Idea of PIG/HIVE

Hadoop is the most important data processing tool for any data scientist, so if you learn data science from scratch, make sure you have a very good idea of the platform. PIG and Hive are the two primary components of Hadoop. You can learn Hadoop through online tutorials or blogs.

Data Science Skill Set

Data Science Skill Set Source – Quora

2. Machine Learning

Machine learning is the next set of concepts that you need to focus on. Machine learning uses AI to get data and statistics. Some basic skills required for machine learning are:

(a) Computer fundamentals 

(b) Programming skills

(c) Knowledge of Probability and Statistics

(d) Data evaluation skills

Joining a machine learning tutorial or attending webinars on the topic can help you get a deeper understanding of these concepts.

3. Data Science Tools

 Lastly, the best way to learn data science is the tools that are used by most organizations. Some popular tools used by Data Scientists include:

(a) SAS

SAS is used for statistical operations and is based on the SAS programming language.

(b) Tableau

Tableau is a powerful visualization tool that helps you design graphs and interactive visualization.

(c) Ggplot2

Ggplot 2 is an advanced visualization package for the R programming language. Ggplot is the most used library by data scientists for creating visualizations.

Practising with your own projects and joining learning communities will help you understand how to use these tools better.

You can use this method to learn data science from scratch or even if you have prior technical experience and want a deeper understanding of the concepts of data science.

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Career Options in the Data Science Landscape

As a data scientist, there are many data science jobs available. You can work in many different industries, and the basic job duties will remain the same in any field. Some popular career choices for data scientists and the associated job duties include:

1. Business Intelligence Developer

Job duties: Designing and developing strategies to assist businesses in finding the information they need.

2. Data Architect

Job duties: Designing analytics applications for different platforms.

3. Data Engineer

Job duties: Performing real-time and batch processing to get relevant data output.

4. Machine Learning Scientist

Job duties: Researching new data systems and algorithms.

5. Data Analyst

Job duties: Transforming data sets to meet the analysis requirements of companies.

Closing Thoughts

The best way to learn data science is by working on projects and practice. With more knowledge, you can get better career opportunities.

To get a more in-depth view of everything that is Data Science, read this Ultimate Guide to Data Science.

Data Science is a vast and varied field, and though new, it has been adopted by many top organizations across the globe. If you decide to learn data science, you will have ample job prospects in numerous industries. Enroll in the Data Science Master Course to create a strong foundation in Data Science & build a successful career as a Data Scientist.




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