• Introduction To Data Science Syllabus Pdf
• Data Science Definition
• Introduction To Data Science Using R Pdf
• Data Science Pdf Download

Learn Introduction to Data Science from IBM. In this Specialization learners will develop foundational Data Science skills to prepare them for a career or further learning that involves more advanced topics in Data Science. Pulled from the web, here is a our collection of the best, free books on Data Science, Big Data, Data Mining, Machine Learning, Python, R, SQL, NoSQL and more. 2.5K SHARES If you’re looking for even more learning materials, be sure to also check out an online data science course through our comprehensive courses list. Google’s self-driving car, Netflix’s Recommendation Engine, and Apple’s Siri—all of these are real-life applications of Data Science. So, in this blog on ‘Introduction to Data Science’, we will start off by understanding the Data Science meaning and then we’ll comprehensively look at the life cycle of Data Science. This book introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression and machine learning and helps you develop skills such as R programming, data wrangling with dplyr, data visualization with ggplot2, file organization with UNIX/Linux shell, version control with GitHub,.

What is Data Science?

The simplest Data Science meaning would be, applying some scientific skills on top of data so that we can make this data talk to us.
Now, what we exactly mean by ‘applying scientific skills on top of data’? Well, to put it precisely, Data Science is an umbrella term which encompasses multiple skills and scientific techniques.

Techniques which Data Science comprises are:

When we combine all of these scientific skills into one, what we get is nothing but Data Science. Now, let’s go ahead and have a look at these different scientific techniques in this blog on ‘Introduction to Data Science’.

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Data Visualization

We’ll start with data visualization. Data visualization is an essential component of a Data Scientist’s skills set. So, in simple terms, data visualization can be considered an amalgamation of science and design in a meaningful way.

Data Manipulation

Next technique in Data Science is data manipulation.
Normally, the raw data which we get from different sources is extremely untidy and drawing inferences from this untidy data is too difficult. This is where data manipulation comes in. Data manipulation techniques help us refine the raw data and make it more organized so that finding insights from the raw data becomes easy.

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Statistical Analysis

Next up in this blog on ‘Introduction to Data Science’ is statistical analysis.
Simply put, statistical analysis helps us understand data through mathematics, i.e., these mathematical equations help in understanding the nature of a dataset and also in exploring the relationships between the underlying entities.

Machine Learning

Finally, we have Machine Learning.
Machine Learning is a sub-field of Artificial Intelligence, where we teach a machine how to learn on the basis of input data. This is where we build scientific models for the purpose of prediction and classification.

Now that we have properly understood the Data Science meaning, it’s time to look at the life cycle of Data Science in the below section: ‘Life Cycle of Data Science’.

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Life Cycle of Data Science

Let’s look at the stages involved in the life cycle of Data Science.

• Model Building
• Pattern Evaluation
• Knowledge Representation

Now, let’s go ahead and understand each of these stages in detail.

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Data Acquisition

We already know that data comes from multiple sources and it comes in multiple formats. So, our first step would be to integrate all of this data and store it in one single location. Further, from this integrated data, we’ll have to select a particular section to implement our Data Science task on.
So, in this step we are acquiring data.

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Data Pre-processing

Once the data acquisition is done, it’s time for pre-processing. The raw data which we have acquired cannot be used directly for Data Science tasks. This data needs to be processed by applying some operations such as normalization and aggregation.

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Model Building

Once pre-processing is done, it is time for the most important step in the Data Science life cycle, which is model building. Here, we apply different scientific algorithms such as linear regression, k-means clustering, and random forest to find interesting insights.

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Pattern Evaluation

After we build the model on top of our data and extract some patterns, it’s time to check for the validity of these patterns, i.e., in this step, we check if the obtained information is correct, useful, and new. Only if the obtained information satisfies these three conditions, we consider the information to be valid.

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Knowledge Representation

Once the information is validated, it is time to represent the information with simple aesthetic graphs.
Thus, we conclude this comprehensive introduction to Data Science.

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Importance of Data Science: The Current Scenario

In a world that is increasingly becoming a digital space, organizations deal with zettabytes and yottabytes of structured and unstructured data every day. Evolving technologies have enabled cost savings and smarter storage spaces to store critical data.

Currently, in the industry, there is a huge need for skilled and certified Data Scientists. They are among the highest-paid professionals in the IT industry. According to Forbes, ‘the best job in America is of a Data Scientist with an average annual salary of $110,000’. Only a few people have the capability to process it and derive valuable insights out of it.

Furthermore, looking at the huge and ever-increasing requirements, McKinsey has predicted that there will be a 50 percent gap in the supply of Data Scientists versus its demand in the upcoming years. That’s why in this blog we are talking about ‘What is Data Science?’

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In recent years, there is a huge growth in the field of Internet of Things (IoT), due to which 90 percent of the data has been generated in the current world. Every day, 2.5 quintillion bytes of data are generated, and it is more accelerated with the growth of IoT. This data comes from all possible sources such as:

• Sensors used in shopping malls to gather shoppers’ information
• Posts on social media platforms
• Digital pictures and videos captured in our phones
• Purchase transactions made through e-commerce

This data is known as big data.

Companies are flooded with colossal amounts of data. Thus, it is very important to know what to do with this exploding data and how to utilize it.

It is here, the concept of Data Science comes into the picture. Data Science brings together a lot of skills like statistics, mathematics, and business domain knowledge and helps an organization find ways to:

• Reduce costs
• Get into new markets
• Tap on different demographics
• Gauge the effectiveness of a marketing campaign
• Launch a new product or service

And the list is endless!

Therefore, regardless of the industry vertical, Data Science is likely to play a key role in your organization’s success.

Look at the below infographic, and you will be able to understand how Data Science is creating its impression:

Introduction To Data Science Syllabus Pdf

How do top industry players use Data Science?

In this section of the ‘What is Data Science?’ blog, we will look at how top industry players like Google, Amazon, and Visa are using Data Science. IT organizations need to address their complex and expanding data environments in order to identify new value sources, exploit opportunities, and grow or optimize themselves, efficiently. Here, the deciding factor for an organization is ‘what value they extract from their data repository using analytics and how well they present it’. Below, we list some of the biggest and best companies that are hiring Data Scientists at top-notch salaries.

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Data Science Life Cycle

For a better understanding of ‘What is Data Science?’, let’s explore its life cycle. Suppose, Mr. X is the owner of a retail store and his goal is to improve the sales of his store by identifying the drivers of sales. To accomplish the goal, he needs to answer the following questions:

• Which are the most profitable products in the store?
• How are the in-store promotions working?
• Are the product placements effectively deployed?

His primary aim is to answer these questions which would surely influence the outcome of the project. Hence, he appoints you as a Data Scientist. Let’s solve this problem using the Data Science life cycle.

Data Discovery

The first phase in the Data Science life cycle is data discovery for any Data Science problem. It includes ways to discover data from various sources which could be in an unstructured format like videos or images or in a structured format like in text files, or it could be from relational database systems. Organizations are also peeping into customer social media data, and the like, to understand customer mindset better.

In this stage, as a Data Scientist, our objective would be to boost the sales of Mr. X’s retail store. Here, factors affecting the sales could be:

• Store location
• Staff
• Working hours
• Promotions
• Product placement
• Product pricing
• Competitors’ location and promotions, and so on

Keeping these factors in mind, we would develop clarity on the data and procure this data for our analysis. At the end of this stage, we would collect all data that pertain to the elements listed above.

Data Preparation

Once the data discovery phase is completed, the next stage is data preparation. It includes converting disparate data into a common format in order to work with it seamlessly. This process involves collecting clean data subsets and inserting suitable defaults, and it can also involve more complex methods like identifying missing values by modeling, and so on. Once the data cleaning is done, the next step is to integrate and create a conclusion from the dataset for analysis. This involves the integration of data which includes merging two or more tables of the same objects, but storing different information, or summarizing fields in a table using aggregation. Here, we would also try to explore and understand what patterns and values our datasets have.

Mathematical Models

Do you know, all Data Science projects have certain mathematical models driving them. These models are planned and built by the Data Scientists in order to suit the specific need of the business organization. This might involve various areas of the mathematical domain including statistics, logistic and linear regression, differential and integral calculus, etc. Various tools and apparatus used in this regard could be R statistical computing tools, Python programming language, SAS advanced analytical tools, SQL, and various data visualization tools like Tableau and QlikView.

Also, to generate a satisfactory result, one model might not be enough. We need to use two or more models. In this scenario, a Data scientist will create a group of models. After measuring the models, he/she will revise the parameters and fine-tune them for the next modeling run. This process will continue until the Data Scientist is pretty sure that he/she has found the best model.

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In this stage, as a Data Scientist, you will build mathematical models based on the business needs of Mr. X, i.e., based on if product A or product B is the most profitable in the store, whether the product placements are effectively working in the store, etc.

Getting Things in Action

Once the data is prepared and the models are built, it is time to get these models working in order to achieve the desired results. There might be various discrepancies and a lot of troubleshooting that might be needed, and thus the model might have to be tweaked. Here, model evaluation explains the performance of the model.

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In this stage, you as a Data Scientist will gather information and derive outcomes based on the business requirements of Mr. X.

Communication

Communicating the findings is the last but not the least step in a Data Science endeavor. In this stage, the Data Scientist needs to be a liaison between various teams and should be able to seamlessly communicate his findings to key stakeholders and decision-makers in the organization so that actions can be taken based on the recommendations of the Data Scientist.

In our example, based on the findings, you will communicate and recommend certain changes in the business strategy so that Mr. X can earn the maximum profit.

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Data Science Components

Now, in this ‘What is Data Science?’ blog, we will discuss some of the key components of Data Science, which are:

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• Data (and Its Various Types)

The raw dataset is the foundation of Data Science, and it can be of various types like structured data (mostly in a tabular form) and unstructured data (images, videos, emails, PDF files, etc.)

• Programming (Python and R)

Data management and analysis is done by computer programming. In Data Science, two programming languages are most popular: Python and R.

• Statistics and Probability

Data is manipulated to extract information out of it. The mathematical foundation of Data Science is statistics and probability. Without having a clear knowledge of statistics and probability, there is a high possibility of misinterpreting data and reaching at incorrect conclusions. That’s the reason why statistics and probability play a crucial role in Data Science.

Data Science Definition

• Machine Learning

As a Data Scientist, every day, you will be using Machine Learning algorithms such as regression and classification methods. It is very important for a Data Scientist to know Machine learning as a part of their job so that they can predict valuable insights from available data.

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• Big Data

In the current world, raw data is compared with crude oil, and the way we extract refined oil from the crude oil, by applying Data Science, we can extract different kinds of information from raw data. Different tools used by Data Scientists to process big data are Java, Hadoop, R, Pig, Apache Spark, etc.

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How does Intellipaat help you in making a career in Data Science?

Now, you can answer the question ‘What is Data Science?’ and know that Data Science is not all about money. It also allows you to gain immense knowledge throughout your career. So, it is this heady mix of money and deep domain knowledge that makes Data Science such an enviable career option for budding technology professionals.

Intellipaat provides huge opportunities to the aspirants who are willing to establish themselves as all-rounders in this area. Hence, getting trained in Data Science technologies through courses offered by Intellipaat will be the best career move you will ever make. Intellipaat offers a wide range of courses dedicated to providing you an end-to-end knowledge about the trending and highly in-demand Data Science skills in this domain.

It was not joking when Harvard Business Review reported that Data Science is the hottest job opportunity of the twenty-first century. Today, if any digitally driven organization is starved of data even for a short duration of time, then it loses its competitive edge. Data Scientists help organizations make sense of their customers, markets, and the business as a whole.

If you want to become a Google Data Scientist at the best salary, then you need to be at the top of your game. If you are wondering how to learn Data Science, then Intellipaat is just the right place to start with your incredible Data Science journey.

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