Data Science Tutorial Guide for Beginner

What is Data Science?

Data Science is being considered as one of the most liked and preferred job for all technocrats, so today we have brought this blog post that can be considered as a guide of this profession. Data Science is the best and most preferred profession that may also need a deep understanding of a few basic concepts.

In this blog post, we will provide an introduction to Data Science along with its job trends and the basic Data Science components. We will also discuss what Data Science is and who can become a data scientist? So, let us start our discussion with a brief introduction to the topic.

The term Data Science involves two mathematical terms one is mathematical statistics, and the other is data analysis. The journey of this complete profile is amazing and can be easily accomplished by technical and non-technical persons. As it is all about machine learning, so future prediction has been made possible by this as well.

As far as Data Science is concerned, then it does mean data-driven science that uses scientific methods, processes, and methods that can be used to extract some useful information either from structured or unstructured data.

Today, we will discuss these analytic processes and methods in this tutorial Guide so that you can become familiar with that.

Who can be Data Scientists?

It’s a well-known fact that data scientist must be proficient in mathematics, must be familiar with business fields and have great computer skills, but sometimes a person cannot have all skills. In such a case, teams are formed so that each team has the experts of every field. But here the fact is that you should be familiar with at least one skill.

In most of the corporates, the complete job of Data Science is divided among teams and as per their expertise, the problems are resolved. Moreover, as per the expertise, one can brush-up his appropriate skill and learn Data Science to become a scientist.

Why should you Learn Data Science?

As today, there is a huge amount of data all over the internet, and companies are storing more data, so organizations analyze it and take out desired and required information from the data repositories. Processing of abundant data is one of the toughest jobs, and therefore, organizations are hiring professionals for their help.

With the help of Data Science, you can understand the customer’s behavior and know their expectations. Their feedback data can be analyzed to know the facts and their expectations. Apart from this, there can be countless benefits of Data Science. You cannot only make better and fruitful decisions but also reduce production cost and give your customer their desired product.

It basically provides the following advantages:

• Reduced Cost
• Focus on Next Product Generation
• Better and Faster Decision Making
• Improved Service or Product

How can Problems be solved in Data Science?

Data Science problems are solved by using algorithms, but here the big problem is to choose the right algorithm. There are manly below-listed problem types to be judged, and scientists have to decide which algorithm should be used for any particular type of problems:

Is this of type A or B?	Classification Algorithms are used
Is the problem weird?	Anomaly Detection Algorithms should be Used
How many or How Much to be Find	Use Regression Algorithms
How to Organize this?	Use Clustering Algorithms
What Should Next be Done?	Reinforcement Learning must be Applied

 

Here, the algorithm’s selection depends on the type of problem. In the next section of this post, we will discuss each of the problems and their solution one by one:

A). Is this of Type A or B?

These are those problems which have an answer either ‘Yes’ or ‘No’ or we can say in 1 or 0, e.g. if the problem is like What will you like to watch either cricket or football then you have only two options here to answer -cricket or football, and the answer cannot be basketball or badminton in any condition.

Hence, you have two options in this, but you have to answer only one like an on/off button (or toggle switch). The problems that have only two types of answers are known as 2-Class Classification problems, while if there exist more than two answers, then it is known as Multi-Class Classification problems. So, in short, we can say that such problems can be solved by using categorical algorithms.

Read: Job Description & All Key Responsibilities of a Data Scientist

B). Is the problem weird?

You might have come across a game “odd one out,” in which you have to find the odd image or thing in the existing image.

The above image shows the “odd one out” concept. What is odd or weird in this image? Redman in the above image is the odd or an anomaly.

Such questions involve patterns that can be solved using Anomaly Detection Algorithms, when there is a break-in pattern, the algorithm flags that particular event for review. Like if there are several transactions to be analyzed, then any weird transaction can be flagged to review. As a result, security measures can be implemented properly, and human efforts can be reduced.

C). How many or how much to be found?

If there is any problem that involves mathematical calculation, then it can be solved by using regression analysis. All problems that involve numerical values and figures can be easily solved by using regression analysis.

For example, if one wants to predict the temperature of the next day or week, then the answer to this question will be a numeric value and regression analysis can help in finding the answer.

D). How to Organize this?

If you have some data and do not have any idea how to use it and does not make any sense, then you may think about how the problem will be solved? It can be solved by using a clustering algorithm. In these solutions, the data are grouped as per their common characteristics, and then the clusters are being formed.

You may clearly see in the above image the three different groups of clusters. Here, why I used “different groups”? Because the cluster groups can be easily differentiated because of the three different colors.  Similarly, with data with any information in it, clustering algorithms try to capture the common in them and clusters them together.

E). What should be done next?

When your computer has to take any decision depending on your problem, then reinforcement algorithms are being used. These algorithms are based on human psychology in which computers like to be appreciated when they are trained. Here, you do not teach computers. Instead, they take their decisions and take the appropriate action.

What are the Components of Data Science?

Data Science is a vast field, and the complete process has a few main components that we are going to discuss in our next section.

1). Datasets

There are lots of data to be analyzed that is fed either through analytics tool or algorithms. The data is fetched by several past researches. Datasets are being formed with the help of such data and then are analyzed.

2). R Language

R is an open-source programming language that is used for statistical computing and graphics that is supported by the R Foundation. R studio uses this language. Mainly the language is being used for the following reasons:

• Statistical and Programming Languages
• Data Analysis and Visualization
• Simple to Learn
• Open Source or Free

R Studio can be used to analyze large datasets that can have structured and unstructured data. Such data is also known as Big Data.

3). Big Data

Big Data is a collection of data sets that are too large and complex, so it becomes difficult to process traditional data and database management. As traditional data cannot be handled by the existing software so a new tool and language can solve it easily.

Read: An Easy Way to Understand Adaboost

4). Hadoop

Hadoop framework can be used to store and process large datasets in distributed and parallel fashion. Hadoop can be used to store and process data for this; it uses HDFS and provides high availability across the distributed ecosystem. MapReduce is used to process data, and it uses the ‘map’ and ‘reduce’ processes to analyze data.

5). Spark R

This R package is a lightweight way to be used with R. It is being used over R applications as it provides a distributed data frame to support selection, aggregation, filtering even on large datasets. Spark R is like the R language and can be used with that as well.

Data Scientist Job Trends

This is clear from the graph that job options are just the plenty for the role of data scientist and they are getting attractive salary packages too as per their skills and experience.

So, you must be pretty much sure now why learning Data Science makes sense. This is not only useful for organizations but had a prosing career choice shortly too. In the next section, we will discuss the various job roles for Data Science experts and their average salaries in Indian and the USA.

Tools for Data Science

Data Science allows us to solve problems with a sequence of steps:

Step1- Collection of data

Step2- Pre-processing of the data

Step3- Analysing data

Step 4- Driving insights and generating reports

Step5- Taking insights based on decisions

Data Science is divided into four main categories, based on which Data Science tools are used.

Data Analysis	R, Spark, Python, and SaS
Data Warehousing	Hadoop, SQL, Hive
Data Visualization	R, Tableau, Raw
Machine Learning	Spark, Azure ML Studio, Mahout

Applications of Data Science

Data Science is a wide concept of modern technology, and it is applicable in the wide range of platforms. Some of the primary applications of Data Science are: -

• Internet research: To search for a specific keyword, Google and other search engines make use of Data Science technology to show the results in a fraction of seconds.
• Suggestions based system: You might have seen “Friend suggestions” on Facebook and “Follow suggestions” on Instagram. This is a suggestions system which also makes use of Data Science technology.
• Image and Speech Recognition: Alexa, Google assistant, and Siri are the best assistant for modern people. Speech and image recognition are also done with the help of Data Science.
• Gaming: From the game development to game monetization, the role of Data Science is enormous. With the growth in gaming users, user’s playing time, interaction time, quitting time, results, scores, etc. analytics are all performed by Data Science only.
• Online Price Comparison: When you search for the comparison of gadgets on Google, the mechanism used by those websites is Machine Learning. Several examples of websites in this criteria are- Junglee, PriceRunner, SmartPrix, Shopzilla, etc. These websites update itself as per the filters you implement for the comparisons.
• Natural Language Processing: NLP or Natural Language Processing is a technology that is focused on the analysis of text-based information. With this technology of Data Science, we can develop intelligent bots that may answer to the queries of the users.
• Self-driving cars: In developed countries like the USA, you may find self-driving cars which are making safer driving environments for the drivers. It also optimizes vehicle performance and adds great anatomy to the drivers.

Challenges of Data Science Technology

As per the research conducted by Kaggle, it is estimated that between 70% to 85% of Data Science projects fail due to either negligence or other challenges failed by Data Scientists. These challenges can be grouped into the following categories: -

Collaboration – 76%

Read: Introduction of Decision Trees in Machine Learning

Data - 68%

Talent – 42%

Tools – 36%

Budget – 27%

Other challenges in Data Science Technology are: -

• High range of information and data is required for the accurate analysis
• There is no appropriate Data Science talent pool available
• Management does not provide financial support for a Data Science team
• Unavailability of data
• Difficult access to data
• Explanation of Data Science to others is difficult
• Privacy issues
• Lack of significant domain expert
• Small organizations do not have a Data Science team
• Cost-factors

Career Tracks with Data Science

Although there are many career tracks associated with Data Science. But here, we are going to discuss two major career tracks which you can opt for your Data Science career. But before choosing your career in Data Science, ask yourself these questions-

• How do I know which machine learning model will work "best" with my dataset?
• How do I interpret the results of my model?
• How do I evaluate whether my model will generalize to future data?
• How do I select which features should be included in my model?

Data Science Career with R

• R is data analysis software: Data scientists, statisticians, and analysts, anyone who wants to make sense of data, can use R for statistical analysis, data visualization, and predictive modeling.
• R is a programming language: It is an object-oriented language created by statisticians. R provides objects, operators, and functions that allow users to explore, model, and visualize data.
• R is an environment for statistical analysis: Standard statistical methods are easy to implement in R, and since much of the cutting-edge research in statistics and predictive modeling is done in R, newly developed techniques are often available in R first.
• R is an open-source software project: R is free and, thanks to years of scrutiny and tinkering by users and developers. It has a high standard of quality and numerical accuracy. R is an open interface that allows it to integrate with other applications and systems.
• R is a community: The R project leadership has grown to include more than 20 leading statisticians and computer scientists from around the world, and thousands of contributors have created add-on packages. With two million users, R boasts a vibrant online community.

Data Science Career with Python

You can opt for several career options after choosing Python as a career track of Data Science. There are frameworks you can learn, which may help you in the advancement of your career using Python for Data Science. Some of the career paths are given below.

• Django for Web Development
• Pygame for Game Development
• Hadoop for Big Data
• Selenium for Web Testing

Various Job Roles for Data Science Experts

The candidates who have the data scientist skills can get various job titles like listed below:

• Data Engineer
• Data Scientist
• Data Architect
• Data Analyst
• Data Administrator
• Business Analyst
• Analytics Manager
• Business Intelligence Manager
• Quantitate Analyst

As per PayScale.com average salary of data scientists in the US and India is shown below:

To take the career opportunity of Data Science, one must keep on updating his skills, and it is quite clear by the above statistics that the person having more skills will have more chances to get higher salaries. Moreover, as the chart is prepared as per skills, so the variation clearly indicated that Python and Machine Learning languages are at the top in India, and the US both.

Job Responsibilities of Data Scientist

• Selecting features, building and optimizing classifiers using machine learning techniques
• Data mining using state-of-the-art methods
• Extending the company’s data with third-party sources of information when needed
• Enhancing data collection procedures to include information that is relevant for building analytic systems
• Processing, cleansing, and verifying the integrity of data used for analysis
• Doing ad-hoc analysis and presenting results in a clear manner
• Creating automated anomaly detection systems and constant tracking of its performance

Skills and Qualifications for Data Scientist

• Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, etc.
• Experience with common Data Science toolkits, such as R, Weka, NumPy, MatLab, etc. (depending on specific project requirements). Excellence in at least one of these is highly desirable
• Great communication skills
• Experience with data visualization tools, such as D3.js, GGplot, etc.
• Proficiency in using query languages such as SQL, Hive, Pig
• Experience with NoSQL databases, such as MongoDB, Cassandra, HBase (depending on project requirements)
• Good applied statistics skills, such as distributions, statistical testing, regression, etc.
• Good scripting and programming skills
• Data-oriented personality

Final Words:

So, here we come to the final section of our blog, and it’s very clear that Data Science can provide you with the most promising career options today. It is not that much difficult to learn Data Science, and any pre-existing skill can help you definitely.

Python and R are the two languages that are being used to analyze the data. So, by learning these languages, you can become a professional Data Scientist. K-Means, Clustering, Decision Tree, Naïve Bayes are a few of the popular algorithms used in Data Science frequently, and practical knowledge can always stand you ahead of the crowd.

Read: An Introduction to the Recurrent Neural Networks