Outline:

Introduction

It’s all around you, everywhere, powering your camera, tracking your smartphone, helping you to navigate your PC. But have you ever considered what actually it is? Well, it's all about data science. Everything we do, from posting on social media to texting to saving a document, generates a huge amount of data we can call big data.

Here, Data Science plays the main role in the story of “Big Data”. Although data science is not a new word since from the past years, it takes the position in all over the world when the first computer was invented, the data scientists ( people who analyze data sets or give structure to that data) make it possible to create a system where this big data can utilize and analyze into a meaningful structure.

What is Data Science?

Basically Data Science provides meaningful information based on large amounts of complex data or big data. It is the field of study that combines Domain Expertise, Programming and knowledge of Mathematics and Statistics to extract meaningful insights from data. So, it is primarily used to make decisions and predictions making use of Predictive Casual Analytics, Prescriptive Analytics(predictive plus decision science) and Machine Learning.

Here what is meant by Prescriptive, Causal Analytics and Machine Learning Predictions. Prescriptive analytics - if you want a model that has the intelligence of taking its own decisions and the ability to modify it with dynamic parameters, you certainly need prescriptive advice.

It not only predicts but suggests associated outcomes, for example, Google’s-driving cars, the data gathered by vehicles can be used to train self-driving cars, you can run algorithms on this data to bring intelligence in it. Predictive causal analytics -  if you want a model that can predict the possibilities of a particular event in the future, you need PCA, one of the best examples is Amazon’s recommendations. When you make a purchase, it puts up a list of other similar items that other buyers purchased.

Machine Learning for Making Predictions - if you have transactional data of a finance company and need to build a model to determine the future trend, the machine learning algorithms are the best bet. It is called Supervised because you already have the data based on which you can train your machines.

Machine Learning for Pattern Discovery - if you don’t have the parameters based on which you can make predictions, then you need to find out the hidden patterns within the data set to be able to make meaningful predictions. This is nothing but the for an unsupervised model as you don’t have any predefined labels for grouping.

What Are The Career Paths Of Data Science?

Data science is considered one of the most demand-able jobs in the industry right now. It is showing more growth in upcoming years. Sounds like an interesting job? Here I will address the ‘big four’ designations and trace their professional career path.

1- Data Scientist

A ‘Data Scientist’ is highly demand-able in any company. That is why this designation is most sought-after by professionals these days. American mathematician and computer scientist DJ Patil defined the role of data scientist as” A unique blend of skills that can both unlock the insights of data and tell a fantastic story via the data”.

In the modern workplace, data scientists also have to build machine learning models for prediction, find patterns and trends in data, visualize data, and even pitch in with marketing strategies. A data scientist's main objective is to organize and analyse large amounts of data, often using software specifically designed for the task.

A data scientists approach to data analysis depends on their industry and specific needs of the business or department they are working. Professional data scientists can work with Artificial Intelligence and Machine Learning with equal ease.

Skill set: Statistics, Mathematics, Data Modelling, Python or R programming, database skills, business acumen. ‌‌

2- Data Analyst‌‌

In 2020 industries are increasingly engaged in data to make critical business decisions like, which new products to develop, new markets to enter, new investments to make, and new customers to target. In these organizations, the job of the data analyst is to assign a numerical value to these important business functions.‌‌

So performance can be assessed and compared over time. But the job involves more than just looking at numbers. An analyst also needs to know how to use data to enable an organization to make more informed decisions. Therefore these roles are in high demand.

Skill set: Data Modelling, Python or R programming, Tableau, database cleaning skills, visualization.

3- Data Engineer‌‌

A data engineer is considered as the backbone of any big organisation. Companies usually hire data engineers to channel their talents towards software development. As a data engineer works with the organization’s core data infrastructure, this role requires a deep knowledge of programming skills. In most organisations, a data engineer is responsible for building data pipelines and correcting the data flow to make sure the information reaches the relevant departments.

Skill set: Database management, database cleaning skills, Hadoop. ‌‌

4- Business Intelligence Developer (BI)‌‌

BI developers design and develop strategies to assist business users in quickly finding the information they need to make better business decisions. BI developers use tools or develop custom BI analytics applications to facilitate the end-users understanding of their systems.‌‌

Overall, the role of business intelligence is to improve all parts of the company by improving access to the firm’s data and then using that data to increase profitability. Companies that employ BI practices can translate their collected data into insights into their business processes. The insights can then be used to create the strategic business decision that improves productivity, increases revenue and accelerates growth.

Skill set: business acumen, visualization.‌‌‌‌

What Are The Data Science Languages?‌‌

Before beginning in Data science, there would be one question that strikes the mind of an aspiring data scientist which is the most well-known language utilized by data scientists? There are many programming languages that are utilized by data scientists like Python, R, C++. Let me go into a detailed description of them.‌‌

1- Python‌‌

Python is an object-oriented, open-source, adaptable and simple to learn programming language. It has a rich arrangement of libraries and tools that makes the assignments simple for Data scientists.‌‌

Additionally, Python has an enormous community base where engineers and data scientists can solicit their queries and answer questions from others. Data science utilizing Python for quite a while and it will keep on being the top choice for Data scientists and Developers.

Data scientists need to manage a large amount of data known as big data. With simple utilization and a huge arrangement of python libraries, python has become a popular choice to deal with big data.‌‌

2- R‌‌

R is a very unique language and has some really interesting features which aren’t present in other languages. The features of R language are very important for data science applications. R was designed as a statistical platform for data cleaning, analysis and representation. ‌‌

Data wrangling is the process of cleaning messy and complex data sets to enable convenient consumption and further analysis. This is very important and time taking process in data science. R has an extensive library of tools for database manipulation and wrangling.‌‌

Data visualization is the visual representation of data in graphical form. This allows analyzing data from angles which are not clear in unorganized or tabulated data. R has many tools that can help in data visualization, analysis and representation. It also provides tools for developers to train and evaluate an algorithm and predict future events in machine learning.‌‌

3- C++‌‌

C++ has found itself an irreplaceable spot in any data ‘Scientist’s Toolkit’. On top of all modern data science, the framework is a layer of low-level programming language known as C++. It is responsible for actually executing the high-level code fed to the framework. ‌‌

This language is simple and extremely powerful and is one of the fastest languages out there. Being a low-level language, C++ allows data scientists to have a much broader command of their applications. For these reasons and more enterprise developers and data scientists with massive scalability and performance requirements tend to be inclined towards the good old C++.‌‌

4- Java‌‌

Java is one of the oldest languages used for enterprise development. Most of the popular and big data frameworks/tools like Spark, Flink are written in java. It has a great number of libraries and tools for machine learning and data science.  ‌‌

Java is usable in a number of processes in the field of data science and throughout data analysis, including cleaning data, data import, statistical analysis, deep learning, Natural Language Processing(NLP), and visualization. Developers consider the Java Virtual Machine as one of the best platforms for machine learning and data science, as it enables the developer to write code that is identical across multiple platforms. ‌‌

Many of the other widely-used programming languages of today for data science and machine learning are not the fastest options. Java is perfectly suited for these speed-critical projects as it is fast executing. Many of the most popular websites and social applications of today rely on java for their data engineering needs, including LinkedIn, Facebook, and Twitter. ‌‌‌‌

Data Science Methodology‌‌

Like traditional scientists, data scientists need a foundational methodology that will serve as a guiding strategy for solving problems. The methodology, which is independent of particular technologies or tools, should provide a framework for proceeding with the methods and processes that will be used to obtain answers and results, such process is called the “Methodology for Data Science”. Basically there are 10 stages in this methodology and they are the following:‌‌

  • Business understanding
  • Analytic approach
  • Data requirements
  • Data collection
  • Data understanding
  • Data preparation
  • Modelling
  • Evaluation
  • Deployment
  • Feedback

Business Understanding: Before solving any problem in the business domain it needs to be understood properly. Business understanding forms a concrete base, which further leads to easy resolution of queries. There should be clarity of what is the exact problem we are going to solve.‌‌

Analytic Approach: Based on the above business understanding one should decide the analytical approach to follow. The approaches can be of 4 types; Descriptive approach(current status and information provided), Diagnostic approach( what is happening, why it is happening), and Prescriptive approach(how the problem should be solved actually).‌‌

Data Requirements: The above chosen analytical method indicates the necessary data content, formats and sources to be gathered. During the process of data requirements, one should find the answers for questions like what, when, why and who.‌‌

Data Collection: Data collection can be obtained in any random format. So, according to the approach chosen and the output to be obtained, the collected data should be validated. Thus, if required one can gather more data or discard the irrelevant data.‌‌

Data Understanding: Data understanding answers the question of the data collected is representative of the problem to be solved?. The data may be descriptive statistics and in this step may lead to reverting the back to the previous step for correction.‌‌

Data Preparation: It is the process in which unwanted data is removed, and only usable data is taken. Here if we don’t need specific data we should not consider it for further process. For example, we delete unwanted pictures from our phone and select those which we want, while from these pictures we select some of them to use on our social apps like Facebook, Instagram etc.

Modelling: Modelling decides whether the data prepared for processing is appropriate or requires more finishing and seasoning. This phase focuses on the building of predictive/descriptive models.‌‌

Evaluation: Model evaluation is done during model development. It checks for the quality of the model to be assessed and also if it meets the business requirements.‌‌

Deployment: As the model is effectively evaluated it is made ready for deployment in the business market. Deployment phase checks how much the model can withstand the external environment and perform well.‌‌

Feedback: Feedback is the necessary purpose which helps in refining the model and accessing its performance and impact.‌‌

Conclusion ‌‌

In the upcoming war of technology, “Data Science” is going upwards to the sky heights, more and more big businesses will be based on data science. As it becomes more famous and adaptable in upcoming years.  Well, it gives us a new way to uncover lots of things in the field of technology, take a step in this world of science and create new ideas.‌‌‌‌