- Intermediate
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- 10 hrs of Learning

We are living in the era of new-age technology, which has brought about an explosion in data. This, in turn, has resulted in the culmination of faster and better technologies and smarter products. The increase in data and the need to harness the same has risen tremendously in the last decade, helping organizations center their business on data, helping make better decisions, and creating newer job roles.

Our Data Science tutorial will help you learn all about data science - what it is, what are the various career paths in data science, the difference between various data science job roles,i.e, data scientist, data analyst, and data engineer and even ways to write a data science resume. The data science tutorial also covers various skills and topics from logistic regression to decision tree and SVM and linear regression. So watch the data science tutorial and learn key skills to become a pro at data science!

- Logistic Regression in R
- Decision Tree in R
- SVM in R
- Hierarchical Clustering in R
- Linear Regression in R
- Random Forest in R

Here are the topics covered in the Data Science tutorial:

- What is Data Science?
- Data Science with R
- Logistic Regression in R
- Linear Regression in R
- Random Forest in R
- Support Vector Machine in R
- Time Series Forecasting in R
- Hierarchical Clustering
- Career in Data Science
- How to Become a Data Scientist?
- Data Scientist vs Data Analyst vs Data Engineer
- Data Science Salary
- Data Science Interview Question

Data Science plays a prominent role in this era of cloud-based business. Whether it is data utilization or management tasks, there are endless avenues in which new applications of this field are regularly developing. When you learn Data Science, it enables you to improvise the decision-making process and even better renders the pattern of discovery and predictive analysis.

The demand for data scientists is quite high. The position has come to play an important role in almost all business sectors, including primary, secondary, and service sectors. Instrumental aspects of this field include the task of collating past and present data to predict future performance and potential.

Today, finding the root causes for any issue has become quite easy with developments in this field. Data scientists take the raw data as the input and transform it into easy solutions to business queries. For example, the aviation industry uses data science to predict any shortcomings in its services and make flying a seamless experience for all their customers.

Such optimization of services includes:

- Route planning and flight scheduling - i.e., whether the flight will be connecting or direct.
- Creation of personalized promotional offers to direct a higher amount of traffic to booking portals- based on individual booking patterns.

It is easy to spot the increasing importance of data science in how seamless online surfing has become with time. Data algorithms play an important role in providing you with customized deals, quality, and delivery times, even in something as simple as buying new furniture for your house.

Following are the steps involved in creating a smooth online experience for consumers through data repositories:

- Data scientists first fetch the questions for understanding any outstanding issues in a better and more cohesive manner.
- Next comes the data collection, both public, and enterprise.
- Further, the data scientist processes the data and provides them with a format suitable for efficient analysis.
- They then feed the data into the ML algorithm or into a statistical model that works with the analytic systems.
- Finally, they prepare the outcome and share it with the concerned department.

For understanding the applications of data science better, let's take a look at the essential aspects of the field:

- Studying Concept: The very first step in projects related to data science is a concept study. The main objective of this step is to study the primary business model to holistically understand the underlying issues. For any analysis, one should be aware of the associated terminologies used and relevant business problems associated with it. This greatly helps in finding the data required for further analysis.
- Data Preparation: Of course, one cannot use the raw data to reach any conclusion. In the overall lifecycle of data science, data preparation is very crucial. This is further classified into different steps like data integration, data transformation, data reduction, and data cleaning.
- Model Planning: The next step is model planning. The selection of the model depends upon the nature and type of the problem.
- Model Building: With the use of effective analytical tools and techniques, data scientists build models wherein they manipulate the data. It helps in finding useful information.
- Communication and Operational Tasks: The last step is to pinpoint the key finding and communicate it to the stakeholders. A data scientist should be proficient in conveying the output to the business-minded audiences. Finally, once the results are accepted, the practical aspect of the analysis is initiated.

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The very first or the entry-level role in this field is that of the data analyst. They are the beginners who transform the numeric data into understandable stuff. The intermediate level consists of data engineers. These engineers are responsible for preparing and pairing the data for further analytical procedures. Data scientists are high-level professionals who use advanced techniques and tools.

Each level requires different proficiency, but advanced-level scientists also need to have some knowledge of machine learning. To reach the highest level of job hierarchy, one needs to possess a specific skill set.

One should have a basic understanding and knowledge about data science, to get started in this field, like:

It is also known as the backbone of Data Science. Data experts should have solid control over machine learning apart from what they know through basic Data Science Tutorials.

Modeling helps in calculating and making predictions based on different algorithmic equations. A knowledge of this is necessary for practical applications.

When one has a firm grasp of statistics, it helps in extracting intelligent and meaningful results from data.

For the successful execution of a Data Science project, a bit of programming knowledge is required.

One should have a fair understanding of the workings of a database, its management, and how to extract data from it.

This "Data Science" tutorial provides basic information on what Data Science is and why one should learn it. It explains the underlying differences between business intelligence and Data Science. The field is so highly in vogue that several sectors have introduced several job openings related to it and are constantly on the lookout for potential candidates. In-depth knowledge of the field is hence a great way to make oneself more employable.

Apart from the basic course program, candidates should hold sufficient knowledge of the overall procedures, tools, and techniques in the field. In this highly advanced era, the demand for data scientists is increasing at a massive rate. Keeping ahead of the curve with a wide skill set is hence of the essence.

Without any doubt, data powers the modern digitalized era. Whether it is making innovative changes or efficient decision-making, data has the potential to unlock varied paths to success and professional fulfillment. In this Data Science tutorial, you will come to know about several job roles, the skills required for them, and the salaries offered in lieu.

According to a recent report, by 2020, Data Science is set to comprise 28% of all digital jobs. It is hence necessary to have clarity related to skills and concepts instrumental to the field. Hence, before embarking on a career in this discipline, one should be aware of the differences between data analysts, scientists, and engineers.

Regression reveals the statistical relationship between variables. Logistic regression, on the other hand, helps in estimating discrete values. It is the classification of the algorithm itself and not the constant variable prediction algorithm. The main topics covered in Logistic regression in R is why the regression is used, what is regression, its types, what is logistic regression, and how it works.

Logistic regression plays a vital role when dealing with data analytics with the help of R programming. You can use this Data Science tutorial to master various aspects of R programming and to explore the data on a broader range.

Before embarking on a career in Data Science, one should be aware of the SVM in R. It is the supervised ML algorithm that analyses and categorizes the data into a binary classifier. While going through this Data Science tutorial, you will come to know about machine learning, its classification, SVM, and linear SVM, an extension of SVM, SVM kernel, their practical uses, and the likes. The data science tutorial is worth the shot, whether you are a beginner or looking to move ahead to the next level.

Factually, we generate quintillion bytes of data every day, and analyzing such huge data is quite challenging. This is not because of the quantity, but because of the vast sources from where this data is derived. Analysts rely on different tools to organize and interpret this bulk amount.

Clustering is one such method of Machine Learning which uses unlabeled data. In this Data Science tutorial, you will learn about clustering, hierarchical clustering, how it works, distance measure, agglomerative clustering, divisive clustering, and their applications using clustering demos. Do check out the tutorials for this.

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Nowadays, companies gather information about their customers to provide better and efficient services to them. In this virtual era, analysts collect and analyze tremendous amounts of data using sophisticated tools, software, and modern techniques. The demand for professionals who can transform basic data analysis into competitive advantages for the company is on the rise.

To become a data scientist, one needs to possess basic skill sets which you can get to know through this Data Science tutorial lesson. Once you master these skills, several job opportunities will open up for you.

The modern economy is increasingly inclined towards analytics, wherein the experts collect data and interpret it. Data scientists are a mix of trend spotters, mathematicians, and computer scientists. Their major role is to analyze bulk data and gain deep insight into what it means. They hence bridge the gap between IT and business.

In this Data Science tutorial, you will get to know about the different job titles, renowned companies who are constantly looking for data scientists, and the bright career outlook of the field. This Data Science tutorial will also give you nuanced insight into the skills required for becoming a high-level professional in the field of Data Science.

Today, one of the most renowned titles in the sectors of Business Intelligence and Data Science is that of a Data Scientist. This role requires mathematical and statistical skills with a touch of engineering tact and persistence from the very processing of data to attaining conclusions.

Nowadays, companies are eager to hire such professionals.

In this chapter of the Data Science tutorial, you will know about the salary structure based on the experience, entry-level, and region of the potential candidate. When the demand is quite high, there is a consequent rise in the salary, and this field generally pays well due to the ever-evolving opportunities.

Glassdoor ranked Data Science as #1 among the 25 best jobs in America, while Harvard referred to it as the 'sexiest job of the 21st century.' Unsurprisingly, the sectors of Data Science, ML, and Big Data are all in high demand in an increasingly digital world.

Companies hiring for these roles tailor questions to test the presence of mind and an in-depth understanding of the candidate's field. In this Data Science tutorial, you will come to know of these questions and the appropriate answers, which will help you ace any interview for the position of a data professional.

To club it in one line (as explained in this data science tutorial) Data Science is all about extracting knowledge from collected data using different methods, tools, and techniques. Sectors like health, gaming, finance, and e-commerce are increasingly reliant on this knowledge.

Conversely, one should have a deep understanding of different domains of business to utilize this data effectively.

The discipline opens up several job opportunities with impressive perks and salaries. And to get started with understanding the roles and basic procedure, and if you want to be a part of this data revolution - our data science tutorial lesson will help you decide on the role best suited to you.

With our Data Science tutorial and certification courses, you can get one step ahead of others and be a part of this dynamic field and promote the successful transformation of the business world with an effective analysis of raw data. Start now - the data science tutorial and your bright future await you!

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