Data Science Tutorial

In this new era of technology, the world is revolving around data. A huge amount of data is generated from different fields such as social media, applications, IOT, industries, etc., and stored in raw data. Extracting the insights and new information from this data is the new attractive task for most companies and society to better. To do this, Data Science comes into the role. Data Science has become one of the most trending jobs of the 21st century, and most companies are looking for skilled candidates for data science job roles. In this tutorial on Data Science, we will introduce you to what exactly data science is? Why is it needed? What are the various data science tools? Different job roles in Data Science and many more.

Let’s Start…

Data Science
Data Science

What is Data Science?

We all interact with the huge amounts of data in our day-to-day life and also generate new data. When we search for something on google, we are interacting with the data, sending an email to someone who is generating new data, making a call, post on Social media, and many more ways. So, there are thousands of ways by which we are generating the data, but how to use this data inefficient way to make our lives easier, and the solution is Data Science.

Hence, “Data Science is an interdisciplinary field that deals with deep study of the huge amount of data and extracts the meaningful insights from the raw data using different scientific methods and technologies. It is used to build useful models or understanding the patterns in data that can be useful for other software applications.” Here, the interdisciplinary field means when people from different fields such as Computer scientists, statisticians, mathematicians, biologists, journalists, sociologists, and many others work together to provide knowledge from data.

Data Science allows us to provide four strategies to explore the world using the data, which are:

  • Probing Reality
  • Pattern Discovery
  • Predicting Future Events
  • Understanding people and the world

Different data sources for gathering the data are the Evolution of technology, IOT, Social Media, and Other factors.

Example: To understand the use of data science in general, we can take the example of Netflix. Netflix uses data science to provide recommendations to the user. When a user searches or watches a series on Netflix, the user’s data and his watch history are saved, which means data is collected. Now it is done with all the users, and according to the interest of each user, they provide recommendations to the users, which

Why Data Science?

In the above section, we understood what data science is, but why it is so trending and becoming the buzzword for everyone, whether technical or non-technical. So, let’s discuss the importance of Data Science.

Before some years ago, when technology was not so evolved, the amount of data generated/per day is very less, and that was manageable and usable with traditional ways such as Excel. As we know, the data is like oil for today’s technology, and humans are generating approximately 2.5 Quintillion bytes of data/day, and this value is increasing day-by-day. So how to manage and use this huge amount of data is mostly in an unstructured form that cannot be applied directly to any model using the traditional methods. Here we need potent algorithms and technology that can analyze, process, and discover patterns from this data, and which is Data Science. Following are some reasons that explain the importance of Data Science:

  • Empowering organizations to make a better decision

Every organization is opting for data science to grow their business faster, as it empowers the organizations to make better decisions. Whether it is a big brand like Google, Amazon, Netflix, etc., to the new star-ups, every company is hiring Data scientists to empower their businesses.

  • Give directions to act based on trends.

If we do hard work in the wrong way, it will not give that appropriate result; the same goes for every business. If we know the future trends in advance, we can get more benefits with a minimum loss, and Data Science does this for us.

  • Identifying the out-of-box Opportunities

It provides out-of-box opportunities in every field, that’s why every field is opting for it, such as it can also be used for automating transportation, e.g., Automatic cars.

  • Available for all the fields

One of the best advantages of Data science is that it can be used in almost all healthcare fields for education or travel.

  • Determining the Target Audiences

Data science helps digital marketers to target audiences. With this, companies can provide the best user experiences and get more benefits.

Data Science Skill Sets/ Prerequisites for Data Science

Technical skills

  • Statistics

Statistics is one of the key skill sets to learn data science. It provides the numbers from the data. One should be familiar with the key concepts of Statistics, such as distribution, Maximum Likelihood, estimators, etc. You should also know the probability and descriptive statistics.

  • Programming Languages

For data science, one should be familiar with some programming language to generate the code for building the models, such as Python, R, Spark, etc. Python & R are the two most common programming languages used in Data Science due to the easy availability of packages and libraries.

Apart from these programming languages, one should also be familiar with database querying languages such as SQL to understand the data.

  • Data Extraction & Processing

The real-world data does not come in the structured format, it means the raw data that we collected from various sources contains lots of inefficient data that can’t be used in the project, so to use it, we need to extract the useful information from raw data, and put them into a structured format so that we can analyze it. The whole process is called data extraction.

  • Data Wrangling & Exploration

Data wrangling is simply a process of cleaning the data. It is one of the most time-consuming processes, as it deals with finding and removing the null values or missing values.

After wrangling the data, we need to explore the data by finding different patterns, outliers, etc.

  • Machine Learning

Machine learning is the core of Data Science. To work with data science, we must know Machine learning algorithms such as Random Forest, KNN, Support Vector Machine, etc. The more you are familiar with these algorithms, the easier the path of Data Science learning will be.

  • Big data processing frameworks

As we know that the data that is being generated in today’s time is a huge amount of data, which cannot be processed with the traditional systems. We need some high-power frameworks to work with this so that we can use Hadoop and Spark.

  • Data Visualization

Data Visualization is a way of representing data in front of end-users in a well-organized format. Data scientists must have this skill to represent the data visually. Tableau and PowerBI are the two tools that are popular for data visualization.

Non-technical Skill

Apart from the technical skills, some non-technical skills are required to learn data science and become a data scientist. Such as:

  • Communication Skill: The better is communication skill, the easier you can communicate. Hence, it is an important skill for a data scientist to explain the project to a team of end-users easily.
  • Critical Thinking: Critical thinking is also just as if you will not think critically; you won’t solve the data-related problems.
  • Curiosity about data: To become a data scientist, you must be curious about what new insights can be found from the data, how to use it in a new way, etc.

Applications of Data Science

As we know, Data science has given a new revolution to the industry, and it is currently being used in approximate every field, from the education sector to Healthcare. Here we will discuss some real-world applications of data science that are already impacting human life. These applications are given below:

  • Recommender System

The recommender system gives recommended suggestions to the relevant items to the users. Such as getting similar product suggestions while purchasing something on Amazon, song suggestions while Listening to songs on Gaana, Movies or Web series suggestions while Searching for a movie on Netflix or Amazon Prime, and many more. All these recommendations are because of data science algorithms. These recommendations are provided to the users based on their search results to improve the user experience.

  • Target Advertisement

Data science is playing a big role in the field of Digital Marketing, and hence in businesses. Seeing different ads on websites or applications, everything is with the use of data science. It targets the audience with their past behavior or past searches. Hence the two different users see the different advertisements on the same website at the same time & place because their search behavior is different, which means one user usually looks for education-related things. He might get ads for different certification courses, and the other user looks more of fashion, then he might get see ads for clothes and other accessories. With a targeted advertisement, the marketers get more revenue as compared to the traditional advertisement system.

  • Image Recognition

When we upload a new image with friends, we immediately started getting suggestions to tag friends; this is because of the Face recognition algorithm, which is a part of Data Science. With the image recognition feature, we can upload and search with an image using the Google Lens on our Android phone or PC.

Image Recognition in Data Science
Image Recognition in Data Science
  • Speech Recognition

When we talk about speech recognition, the name immediately strikes in our mind are Alexa, Siri, Cortana, etc., which are the best examples of Speech recognition. With our voice only, we can play songs, search for something, call someone, etc., without using the text. Speech recognition is the part of Data science that is making our day-to-day life easier.

  • Gaming

Data science is also using in the Gaming sector. Games are now designed with machine learning algorithms that given another level of gaming experience to the users.

  • Airline Route Planning

Airline companies also started using data science algorithms to get maximum benefits and prevent any losses. With data science, airline companies can predict the flight delay and decide which class of airplanes to buy, predict the prices, etc.

  • Fraud and Risk Detection

Fraud and Risk are the two main loopholes of the finance industry. To prevent losses from these two, the finance industry is using data science. With data science, they can detect any fraud on the customer sides and various risks in Finance dealing.

  • Internet Search

When we want to know something related to anything, we just “Google” it and get the result in a fraction of seconds. It is not only Google but also other search engines such as Bing, Yahoo, etc. So, the instant result of our query is that Google uses Data Science algorithms to provide the most appropriate result with minimum time.

Job roles in Data Science

Data science is an interdisciplinary and a very vast field; hence it provides multiple job roles for the candidates. Each job role is assigned to different tasks and have some specific capabilities. These job roles are:

  • Data Scientists
    • Data scientist’s job role is one of the most demanding professions today. As per the different market statistics, this is one of the best job roles for someone.
    • Job tasks for data scientists and Data analysts are somewhere similar.
    • A data scientist’s job responsibility is about understanding business problems and providing the best solution using data analysis and data processing.

The skill required- R, SAS, Python, SQL, MATLAB, Hive, Pig, and Spark.

  • Data Analysts
    • Data analyst’s job role performs various tasks such as visualization, processing a huge amount of data, etc.
    • They also need to run database queries whenever required.
    • They can modify algorithms that can be used to extract information from a huge database without corrupting the data, and doing this requires optimization skills.

Skill RequiredSQL, R, SAS, Python, and good problem-solving capabilities require the data analyst job role.

  • Data Architect
    • The data architect is responsible for creating the blueprints of data management. It helps to integrate, centralize, and protect the database easily.

The skill required- Hive, Pig, Spark, Data warehousing, Data Modelling.

  • Data Engineer
    • Data Engineers are responsible for designing, building, and managing the big data infrastructure. They transform big data into an easily understandable format.
    • They provide large complex datasets to data scientists as per the business requirement.
    • They also develop useful tools to extract useful insights from the data, which can help the data scientists.

The skill required- Database system, data modeling & ETL tools, Data APIs, Data Warehousing, and knowledge of different languages such as SQL, Hive, Pig, R, SAS, Java, Ruby, C++, and MATLAB.

  • Statistician
    • A statistician job role is a little different from other job roles, such it required an understanding of Statistical theories and data organization.
    • They need to extract new insights from the data and provide the new methodologies to engineers to make the task easy.

Skill Required- Data Visualization, Statistical theory & methodology, Data Mining &Machine Learning, Database system, Cloud tools, and knowledge of different languages, such as R, SAS, Python, and SPSS MATLAB, Pig, Hive, SQL, and Perl.

  • Database administrator
    • As the name suggests, the database administrator job role is related to the database.
    • They need to ensure the proper functioning of an organization’s database and grants and revokes its access to other employees as per the requirement.
    • They also handle the database backups and recoveries and ensure that the database is available to all the relevant users.

Skill-required: Backup & Recovery, Data Security, Data Modelling & design, ERP & business knowledge, and knowledge of different languages, such as SQL, Java, Python, Ruby, XML, C#.

  • Business Analyst
    • The business analyst job responsibilities are somewhere similar to that of data analysts, and they are known as the intermediator between the business and IT.
    • They are responsible for providing technology-based solutions to the business team so that they can enhance the business.

Skill-required: Basic MS tools such as MS office, Data Visualization tools such as Tableau, BI understanding, Data Modelling.

  • Machine Learning Engineer
    • Machine Learning Engineers have a high demand in the market, and they make use various machine algorithms to draw various patterns and insights from data.
    • They are responsible for designing or implementing ML applications or algorithms such as Clustering, Polynomial Regression, etc.

Skill Required: Machine Learning algorithms, Statistics & Mathematics, Python, Java, Scala, C++, and JavaScript.

  • Data and Analytics Manager
    • Data and analytics managers have responsibilities of managing and assigning duties & operations to the data science team.
    • They are also the leader of cross-functional projects that have requirements of data.

Skill Required: Database systems, Leadership & project Management, Interpersonal communication, Data Mining & predictive modeling, and the knowledge of various languages such as SQL, R, SAS, Python, MATLAB, Java.

Data Science Life cycle

The Data Science life cycle is about the complete step-by-step process of a data science project. The life cycle includes various stages, and each stage has its own significance. These stages are given below:

Data Science-lifecycle
Data Science-lifecycle
  • Business Requirement/Problem Identification

The first stage of the life cycle is identifying the problem or the requirement for the business. The better we will understand the problem, the more accurate model will get, so it important to have a clear business goal.

In this stage, we find out the variables that need to be predicted and the project’s final objective.

  • Data Acquisition

Data acquisition is a process of collecting data from different sources. So, as we are aware of our final goal, we will collect data accordingly. Such as suppose we are doing a finance company project so we will collect data on previous customers.

There are different ways of collecting the data, but the most convenient way is to collect it directly from the stored files. We can do this by downloading the available datasets from various data sources such as Kaggle in CSV or TSV format.

  • Data processing

The data that we have gathered in the previous stage cannot be directly applied to the model. After collecting it, we need to process the data as it contains various null values, mostly in an unstructured format. We need to transform the gathered data into the desired format. It is one of the most time-consuming processes. It performs the following operations:

  • Data Cleaning
  • Data Reduction
  • Data Transformation
  • Data Integration
  • Data Exploration

Once we clean and process the data, we are ready to analyze it. In the data exploration stage, we analyze the data to understand the different patterns within it. We explore the different features and variables of the dataset using bar-graphs, scatter plots, etc.

We can use various other visualization techniques in this step.

  • Data Modelling

Data modeling is the heart of the complete life cycle, as in this stage, we choose the appropriate model as per the problem. To do this, we perform model training that involves various steps:

  • Firstly, we divide the dataset into a training dataset and a test dataset.
  • Build the model using the training dataset
  • Evaluate the model using the test dataset

It involves using various machine learning algorithms such as Classification, Regression, and Clustering algorithms to build the model.

  • Model Deployment

Once the model is evaluated, we are now ready to deploy it. Model is deployed for the final production or test environment. In this step, we evaluate the model’s performance, and if it is not as per the requirement, we improve it.

Before the final deployment of the model, we need to check whether it matches our project’s objective.

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