Data Science
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What is Data Science?
Data science is a multidisciplinary approach to extracting actionable insights from the large and ever-increasing volumes of data collected and created by today’s organizations. Data science encompasses preparing data for analysis and processing, performing advanced data analysis, and presenting the results to reveal patterns and enable stakeholders to draw informed conclusions.
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.
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 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
Which are important aspects to learn about Data Science?
Data science is an interdisciplinary field concerned with extracting knowledge or insights from data and is an inclusive term for quantitative methods, such as statistics, operations research, data mining, and machine learning, along with the main analyses of each discipline.
Let’s go through the top seven essential and most-looked-for skills that every data scientist should have.
fundamentals of Data Science
Statistics and Probability
Machine Learning and Its Limitations
Programming Skills and Knowledge
Structured Query Language (SQL)
Data Visualization
Data Cleaning
Different tools/programs in data science.
SAS
Apache Spark
BigML
D3.js
MATLAB
Excel
ggplot2
Tableau
Jupyter
Matplotlib
NLTK
Scikit-learn
SAS
It is one of those data science tools which are specifically designed for statistical operations. SAS is a closed source proprietary software that is used by large organizations to analyse data. SAS uses the base SAS programming language for performing statistical modelling.
It is widely used by professionals and companies working on reliable commercial software. SAS offers numerous statistical libraries and tools that you as a Data Scientist can use for modelling and organizing your data.
Apache Spark
Apache Spark or simply Spark is an all-powerful analytics engine and it is the most used Data Science tool. Spark is specifically designed to handle batch processing and Stream Processing.
It comes with many APIs that facilitate Data Scientists to make repeated access to data for Machine Learning, Storage in SQL, etc. It is an improvement over Hadoop and can perform 100 times faster than MapReduce.
Spark has many Machine Learning APIs that can help Data Scientists to make powerful predictions with the given data.
BigML
BigML is another widely used Data Science Tool. It provides a fully interactable, cloud-based GUI environment that you can use for processing Machine Learning Algorithms. BigML provides standardized software using cloud computing for industry requirements.
BigML specializes in predictive modelling. It uses a wide variety of Machine Learning algorithms like clustering, classification, time-series forecasting, etc.
D3.js
JavaScript is mainly used as a client-side scripting language. D3.js, a JavaScript library allows you to make interactive visualizations on your web browser. With several APIs of D3.js, you can use several functions to create dynamic visualization and analysis of data in your browser.
Another powerful feature of D3.js is the usage of animated transitions. D3.js makes documents dynamic by allowing updates on the client-side and actively using the change in data to reflect visualizations on the browser.
MATLAB
MATLAB is a multi-paradigm numerical computing environment for processing mathematical information. It is a closed-source software that facilitates matrix functions, algorithmic implementation and statistical modelling of data.
In Data Science, MATLAB is used for simulating neural networks and fuzzy logic. Using the MATLAB graphics library, you can create powerful visualizations. MATLAB is also used in image and signal processing.
Excel
Excel is a powerful analytical tool for Data Science.
Excel comes with various formulae, tables, filters, slicers, etc. You can also create your custom functions and formulae using Excel. While Excel is not for calculating a huge amount of data, it is still an ideal choice for creating powerful data visualizations and spreadsheets.
You can also connect SQL with Excel and can use it to manipulate and analyse data. Scientists use Excel for data cleaning as it provides an interactable GUI environment to pre-process information easily.
ggplot2
ggplot2 is an advanced data visualization package for the R programming language. The developers created this tool to replace the native graphics package of R and it uses powerful commands to create illustrious visualizations.
Using ggplot2, you can annotate your data in visualizations, add text labels to data points and boost the intractability of your graphs. You can also create various styles of maps such as choropleths, cartograms, hex bins, etc. It is the most used data science tool.
Tableau
Tableau is a Data Visualization software that is packed with powerful graphics to make interactive visualizations. It is focused on industries working in the field of business intelligence.
The most important aspect of Tableau is its ability to interface with databases, spreadsheets, OLAP (Online Analytical Processing) cubes, etc. Along with these features, Tableau can visualize geographical data and for plotting longitudes and latitudes in maps.
Jupyter
Project Jupyter is an open-source tool based on IPython for helping developers in making open-source software and experiences interactive computing. Jupyter supports multiple languages like Julia, Python, and R.
Using Jupyter Notebooks, one can perform data cleaning, statistical computation, visualization and create predictive machine learning models. It is 100% open-source and is, therefore, free of cost.
Matplotlib
Matplotlib is a plotting and visualization library developed for Python. It is the most popular tool for generating graphs with the analyzed data. It is mainly used for plotting complex graphs using simple lines of code. Using this, one can generate bar plots, histograms, scatterplots etc.
Matplotlib is a preferred tool for data visualizations and is used by Data Scientists over other contemporary tools.
NASA used Matplotlib for illustrating data visualizations during the landing of Phoenix Spacecraft. It is also an ideal tool for beginners in learning data visualization with Python.
NLTK
Natural Language Processing has emerged as the most popular field in Data Science. It deals with the development of statistical models that help computers understand human language.
NLTK is widely used for various language processing techniques like tokenization, stemming, tagging, parsing and machine learning. It consists of over 100 corpora which are a collection of data for building machine learning models.
Scikit-learn
Scikit-learn is a library-based in Python that is used for implementing Machine Learning Algorithms. It is simple and easy to implement a tool that is widely used for analysis and data science.
It supports a variety of features in Machine Learning such as data pre-processing, classification, regression, clustering, dimensionality reduction, etc.
To qualify Data Science which skills are important for an individual
Data Science – The Process of Capturing, Analysing and Presenting Business Intelligence with Skill
01. Capture
- Programming and Database skills
- Business Domain Expertise and Knowledge
- Data Modelling, Warehouse, and Unstructured Data Skills
02. Analyse
- Statistical Tool Skills
- Math skills
03. Present
- Visualization Tool Skills
- Storytelling Skills
Different Job Roles in Data Science and its skills requirement from the Industry
A Strong Business Acumen
Great Data Intuition
Strong Communication Skills
Non-Technical Skills Required to Become a Data Scientist
What is the projection of Job opportunities by Industry?
India is the second-highest country next to the US to have generated the demand to recruit about 50,000 Data Scientists for 2020 and 2021. The average salary for a Data Scientist with less than 1 year of experience is about 7 LPA in the Indian market. Even Startups are not shying away to offer a well-trained Data Scientist up to 5 LPA with zero real-time experience. Mid-Level – 3 to 5 years of experience will easily fetch a job for a salary anywhere between 10 LPA – 14 LPA.
Data Science opportunities Industry-wise :
- HealthCare : Industry alone will generate about 20,000 job openings.
- Automotive : This Industry looks forward to creating at least 5000 Jobs by the end of 2020.
- Software Development : The most pivotal which alone will create close to 1 lakh job opportunities.
- There are about 2,50,000 plus E-Commerce companies Globally hence it is evident that there’s a huge demand for Data Scientists as these companies analyse huge amounts of data every day.
