Data Science is a branch of science that uses mathematics and statistics to extract useful information from data. It basically gives you a lot of complicated facts. Data science is a process that contains the following steps: data capture (data acquisition, data entry, and data extraction).

1. Data cleansing, staging, processing, and architecture are all part of data maintenance.

2. Data mining, clustering, modelling, and summarization are all examples of data processing.

3. Predictive analysis, regression, text mining, and qualitative analysis are all examples of data analysis.

4. Data reporting, visualisation, business intelligence, and decision mining are all examples of data communication.

There are different ways to learn data science, go to university, follow a bachelor or master in data science, get into a Bootcamp program, or learn it by yourself.

Introduction Of Data Science

In the booming world of digital commerce, data has generated interest in every domain possible. With an endless supply of information in the form of unorganized information, the requirement to transform it into practical knowledge is more important than ever.

The era of big data began, and as its storage requirements grew, in a world of data where businesses deal with petabytes and exabytes of data. Up until 2010, the storage of data for various businesses was a significant difficulty and source of worry. After storage became a non-issue because to frameworks like Hadoop and others, attention turned to data processing. Here, data science is crucial. The flashy sci-fi movies you enjoy watching can all become true thanks to data science. Its growth has been accelerated in many ways recently, so it is important to understand it and how we may contribute to it if we want to be prepared for the future.

Data Science is an emerging topic that is becoming more and more significant by the day. It is the newest popular phrase in the field of information technology (IT), and market demand for it has been constantly rising. Because businesses need to turn data into insights, there is a growing demand for data scientists. Google, Amazon, Microsoft, and Apple are some of the organizations that hire the most data scientists. Additionally, data science is growing in popularity among experts in information technology.

In the video below, we explain what data science is and how it is used in many projects at tech companies

As explained above, the demand for data scientists is increasing rapidly in almost every industry. As data is increasing, so too much demand for data science.

Recent years have seen a rise in the importance of data science, thanks to the expansion of big data and the accessibility of strong computing resources. As a result, there is a rising need for workers with data science knowledge and skills, and the discipline of data science has grown in-demand.

A brief overview to data science is provided in this article, together with information on data science job responsibilities, tools, components, applications, and other related topics.

What is Data Science?

Data science is a discipline that combines domain knowledge, programming abilities, and math and statistics knowledge to extract useful insights from data. Machine learning algorithms are used to numbers, text, photos, video, audio, and other data to create artificial intelligence (AI) systems that can execute jobs that would normally need human intelligence. As a result, these systems produce insights that analysts and business users may turn into real-world commercial value.

Companies are racing to exploit the insights in their data as data grows at an alarming rate. Most businesses, on the other hand, are short on professionals to analyse their big data for insights and to investigate issues they didn't even realise they had. Organizations must integrate predictive insights, forecasting, and optimization strategies into business and operational systems to appreciate and exploit the value of data science. Many companies are now providing platforms to their knowledge employees that allow them to execute their own machine learning projects and activities. An organization's competitive edge will come from being able to extract trends and opportunities from the huge amounts of data being poured into it.

Learn More️

Data preparation can involve cleansing, aggregating, and manipulating it to be ready for specific types of processing. Analysis requires the development and use of algorithms , analytics and AI models. It’s driven by software that combs through data to find patterns within to transform these patterns into predictions that support business decision-making. The accuracy of these predictions must be validated through scientifically designed tests and experiments. And the results should be shared through the skillful use of data visualization tools that make it possible for anyone to see the patterns and understand trends.

As a result, data scientists (as data science practitioners are called) require computer science and pure science skills beyond those of a typical data analyst. A data scientist must be able to do the following:

🚀 Apply mathematics, statistics, and the scientific method
🚀 Use a wide range of tools and techniques for evaluating and preparing data—everything from SQL to data mining to data integration methods
🚀 Extract insights from data using predictive analytics and artificial intelligence (AI), including machine learning and deep learning models
🚀 Write applications that automate data processing and calculations
🚀 Tell—and illustrate—stories that clearly convey the meaning of results to decision-makers and stakeholders at every level of technical knowledge and understanding
🚀 Explain how these results can be used to solve business problems

Why Become a Data Scientist?

Glassdoor ranked data scientist among the top three jobs in America since 2016.4 As increasing amounts of data become more accessible, large tech companies are no longer the only ones in need of data scientists. The growing demand for data science professionals across industries, big and small, is being challenged by a shortage of qualified candidates available to fill the open positions

The need for data scientists shows no sign of slowing down in the coming years. LinkedIn listed data scientist as one of the most promising jobs in 2021, along with multiple data-science-related skills as the most in-demand by companies.

Learning Data Science

Data is gathered from various industries, channels, and platforms, such as mobile devices, social networks, e-commerce platforms, medical surveys, and searches on the web. The growth in data availability paved the way for a new field of research focused on big data—massive sets of information that help with the development of improved operational tools across all industries.

How does Data Science Works ?

1. Raw data that explains the business issue is acquired from many sources.

2. To find the best solutions that adequately explain the business problem, data modeling is carried out using a variety of statistical analysis and machine learning techniques.

3. Actionable insights that will help solve the business issues identified by data science.

Lifecycle of Data Science

Data Science Lifecycle revolves around the use of machine learning and different analytical strategies to produce insights and predictions from information in order to acquire a commercial enterprise objective.

Phase 1-Discovery:Before you begin the project, it is important to understand the various specifications, requirements, priorities and required budget. You must possess the ability to ask the right questions. Here, you assess if you have the required resources present in terms of people, technology, time and data to support the project. In this phase, you also need to frame the business problem and formulate initial hypotheses (IH) to test.

Phase 2-Data preparation: In this phase, you require analytical sandbox in which you can perform analytics for the entire duration of the project. You need to explore, preprocess and condition data prior to modeling. Further, you will perform ETLT (extract, transform, load and transform) to get data into the sandbox.

You can use R for data cleaning, transformation, and visualization. This will help you to spot the outliers and establish a relationship between the variables. Once you have cleaned and prepared the data, it’s time to do exploratory analytics on it.

Phase 3-Model planning: Here, you will determine the methods and techniques to draw the relationships between variables. These relationships will set the base for the algorithms which you will implement in the next phase. You will apply Exploratory Data Analytics (EDA) using various statistical formulas and visualization tools.

Phase 4-Model building: In this phase, you will develop datasets for training and testing purposes. Here you need to consider whether your existing tools will suffice for running the models or it will need a more robust environment (like fast and parallel processing). You will analyze various learning techniques like classification, association and clustering to build the model

Phase 5-Operationalize: In this phase, you deliver final reports, briefings, code and technical documents. In addition, sometimes a pilot project is also implemented in a real-time production environment. This will provide you a clear picture of the performance and other related constraints on a small scale before full deployment.

Phase 6-Communicate results: Now it is important to evaluate if you have been able to achieve your goal that you had planned in the first phase. So, in the last phase, you identify all the key findings, communicate to the stakeholders and determine if the results of the project are a success or a failure based on the criteria developed in Phase 1.

Prerequisites For Data Science

To effectively implement data science technologies in a business, a number of conditions must be met. Some of the requirements are as follows:

1. Machine Learning

   Machine Learning, a critical component of data science, enables accurate forecasting and estimation. If you want to be successful in the field of data science, you must have a solid understanding of machine learning.

2. Statistics

   If you are serious about pursuing a career in data science, you must possess understanding of both descriptive and inferential statistics. You can draw a variety of conclusions and comprehend the data at hand with the aid of statistical analysis. One illustration would be how we talked about using hypothesis testing to determine whether or not a time series is stationary.

3. Computer Programming

   Professionals must be knowledgeable in programming languages like Python or R to perform the statistical calculations and calculations needed for Data Science operations. You may easily build machine learning models from scratch with the assistance of libraries and scripting experience. Some of the built-in Python programming libraries that can be used for Data Science with Python are Scikit-learn, Tensorflow, pandas, matplotlib, seaborn, scipy, numpy, etc.

4. Mathematical Modeling

   Applying mathematical models based on the information you already have, you may swiftly compute and make predictions. Modeling is useful for figuring out how to train these models and which method will handle a certain problem the best.

5. Databases

   Data science requires a thorough understanding of databases, such as SQL, in order to obtain and deal with data.

6. Analytical Thinking

   To address the business issues, a data scientist must use analytical thinking.

7. Critical Thinking

   It is also necessary for a data scientist to be able to come up with a variety of innovative solutions that are effective.

8. Communication skills

   The ability to communicate effectively is crucial for a data scientist since, after solving a business challenge, you must share your findings with the team.

9. Strong Business Sense

   A data scientist must possess tactical business consulting skills. Data scientists are uniquely positioned to learn from data because they work so closely with it. As a result, it becomes your obligation to turn your observations into common knowledge and contribute to the development of a strategy for resolving important business issues. This indicates that using data to persuasively communicate a story is a key ability of data science. No data-pumping; instead, give a coherent story of the issue and its resolution, using data insights as guiding pillars.

Data science tools

Data scientists must be able to build and run code in order to create models. The most popular programming languages among data scientists are open source tools that include or support pre-built statistical, machine learning and graphics capabilities.

There are three primary skills needed for Data Science
1) A programming language used in the data ecosystem, typically one of Python/R or Scala
2) SQL, for data manipulation and extraction, and
3) Statistics & Machine Learning

• R: An open source programming language and environment for developing statistical computing and graphics, R is the most popular programming language among data scientists. R provides a broad variety of libraries and tools for cleansing and prepping data, creating visualizations, and training and evaluating machine learning and deep learning algorithms. It’s also widely used among data science scholars and researchers.

• Python: Python is a general-purpose, object-oriented, high-level programming language that emphasizes code readability through its distinctive generous use of white space. Several Python libraries support data science tasks, including Numpy for handling large dimensional arrays, Pandas for data manipulation and analysis, and Matplotlib for building data visualizations.

• SQL: SQL is an important skill to learn for every data scientist. They leverage it for transforming and extracting data out of databases. It is one of the most commonly asked subjects in data science interviews

Statistics & Machine learning: If you haven’t had many statistical courses during your prior studies, it is helpful to go through an introductory Statistics and Machine Learning course covering the following topic regression (linear/logistics), decision trees, random forest, k-means and KNN

What are the data science techniques?

Data science experts must be knowledgeable with a wide range of approaches in order to perform their duties.Machine learning algorithms play an important role in data science. In machine learning, data sets are learned about and then algorithms search for patterns, anomalies, or insights in them. It combines supervised, unsupervised, semisupervised, and reinforcement learning techniques, with the algorithms receiving varying degrees of data scientist training and supervision. Some of the more well-liked methods are as follows:

Regression

Discovering a connection that connects two apparently independent data points is done through regression. The relationship is typically depicted as a graph or a series of curves and is fashioned after a mathematical formula. Regression is used to forecast the value of the other data point when the value of the first data point is known. For instance:

1. the speed at which airborne illnesses spread.

2. the connection between employee count and customer happiness.

3. the correlation between the quantity of fire stations and the amount of fire-related injuries in a specific area.

Clustering

Unsupervised learning employs the data science approach of clustering, often known as cluster analysis. In a cluster analysis, objects from a data collection that are closely related are grouped together, and then each group is given a set of properties. Data patterns are revealed through clustering, which is frequently used with big, unstructured data sets.

Classification

Data is categorized when it is put into distinct groups or categories. To recognize and organize data, computers are trained. Building decision algorithms in a computer that swiftly analyses and organizes the data makes use of known data sets. Consider categorizing comments on social media as favorable, negative, or neutral.

Where Do You Fit in Data Science?

Data is everywhere and expansive. A variety of terms related to mining, cleaning, analyzing, and interpreting data are often used interchangeably, but they can actually involve different skill sets and complexity of data.
Data Scientist: Data scientists examine which questions need answering and where to find the related data. They have business acumen and analytical skills as well as the ability to mine, clean, and present data. Businesses use data scientists to source, manage, and analyze large amounts of unstructured data. Results are then synthesized and communicated to key stakeholders to drive strategic decision-making in the organization.
Skills needed: Programming skills (SAS, R, Python), statistical and mathematical skills, storytelling and data visualization, Hadoop, SQL, machine learning

Data Analyst
Data analysts bridge the gap between data scientists and business analysts. They are provided with the questions that need answering from an organization and then organize and analyze data to find results that align with high-level business strategy. Data analysts are responsible for translating technical analysis to qualitative action items and effectively communicating their findings to diverse stakeholders.
Skills needed: Programming skills (SAS, R, Python), statistical and mathematical skills, data wrangling, data visualization

Data Engineer
Data engineers manage exponential amounts of rapidly changing data. They focus on the development, deployment, management, and optimization of data pipelines and infrastructure to transform and transfer data to data scientists for querying.
Skills needed: Programming languages (Java, Scala), NoSQL databases (MongoDB, Cassandra DB), frameworks (Apache Hadoop)

Difference Between Business Intelligence and Data Science

Business intelligence is a combination of the strategies and technologies used for the analysis of business data/information. Like data science, it can provide historical, current, and predictive views of business operations. However, there are some key differences.

Business Intelligence	Data Science
Uses structured data	Uses both structured and unstructured data
Analytical in nature - provides a historical report of the data	Scientific in nature - perform an in-depth statistical analysis on the data
Use of basic statistics with emphasis on visualization (dashboards, reports)	Leverages more sophisticated statistical and predictive analysis and machine learning (ML)
Compares historical data to current data to identify trends	Combines historical and current data to predict future performance and outcomes

Applications of Data Science

Data science has found its applications in almost every industry.

Healthcare
Healthcare companies are using data science to build sophisticated medical instruments to detect and cure diseases.

Gaming

Video and computer games are now being created with the help of data science and that has taken the gaming experience to the next level

Logistics

Data Science is used by logistics companies to optimize routes to ensure faster delivery of products and increase operational efficiency.

The main purpose of frameworks is to make a developer’s job easier by developing a set of conventions that can be adopted for many of the different processes involved in creating a website—from how information is displayed to how it is stored and accessed in the database.

Data science and cloud computing

Cloud computing is bringing many data science benefits within reach of even small and midsized organizations. Data science’s foundation is the manipulation and analysis of extremely large data sets; the cloud provides access to storage infrastructures capable of handling large amounts of data with ease. Data science also involves running machine learning algorithms that demand massive processing power; the cloud makes available the high-performance compute that’s necessary for the task. To purchase equivalent on-site hardware would be far too expensive for many enterprises and research teams, but the cloud makes access affordable with per-use or subscription-based pricing. Cloud infrastructures can be accessed from anywhere in the world, making it possible for multiple groups of data scientists to share access to the data sets they’re working with in the cloud—even if they’re located in different countries. Open source technologies are widely used in data science tool sets. When they’re hosted in the cloud, teams don’t need to install, configure, maintain, or update them locally. Several cloud providers also offer prepackaged tool kits that enable data scientists to build models without coding, further democratizing access to the innovations and insights that this discipline is making available.

💡 Tips to Crack the data science interview

Be Thorough with your Data Science Resume

The absolute basics of any interview, and especially a data science one. You should be able to explain everything listed on your resume. Anything that you could possibly reference, you should be able to speak about it.

Study up on your Data Science Projects

Much like the other details on your resume, deciding what projects to talk about in your interview is also crucial. If there are any projects irrelevant to the role you’ve applied to, adding it in anyway isn’t a great practice. This just shows your interviewer that you cannot prioritize well.

Prepare to Face Case Studies for Data Science Roles

Organizations use case studies as a means of evaluating candidates on how they approach real-life problems. Case studies are the closest thing to the problems that you would be encountering in your role later on. I have seen freshers struggle the most with this part of the data science interview process.

Review Confusing Data Science Terms

Are there any data science terms that have bamboozled you before? I’m sure there are a few – this is true for even experienced data scientists.

Conclusions

Data science is the secret sauce for any firm that wants to grow by becoming more data-driven. Data science initiatives can increase the return on investment by developing data products as well as providing direction through data insight. Hiring individuals with this potent combination of diverse skills, however, is more difficult than it sounds. The demand for data scientists simply outweighs the supply because their salaries are quite high. As a result, when you are able to hire data scientists, take care of them.

A business can grow significantly using data science tools and methods. Every company is going through a digital transformation, and there is a growing need for people with the necessary knowledge and abilities. Companies are willing to pay top dollar for the right talent. If data science is something you're interested in pursuing professionally or if you want to change careers to become a business analyst, data analyst, data engineer, analytics engineer, etc.

Good Luck & Happy Learning!!