Data Science Training in Chennai

TROY INFOTECH provides the best Data Science training in Chennai with Placements. We offer the best Data Science training in Chennai, based on current industry standards, which helps students secure their dream workplace at multinational companies like TCS, CTS, INFOSYS, WIPRO, IBM, and CAPGEMINI. Troy Infotech is one of the most trusted Data Science training institutes in Chennai, providing hands-on knowledge and complete assistance with basic Data Science training in Chennai, as well as advanced Data Science training in Chennai

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Data Science training in Chennai

At Troy Infotech, Data Science training is provided by expert professionals, subject matter experts with more than 7 years of real-time Data Science Project experience. Troy Infotech implements a combination of academic learning and hands-on sessions to provide the student with an ideal exposure that helps transform inexperienced students into full-time professionals who are easily recruited in the industry, and so we could say ourselves the best Data Science training institute in Chennai.

At the well-equipped Troy Infotech, candidates learn the skills for Data Science Concepts, Data Science Fundamentals, Essentials of Programmed-Oriented Object, Writing Python classes, packages, exception handling, Python I/O operations, Class Object programming, Python Data Analysis, Pandas  programming, Python utility or collections framework package, generic, built-in classes,Pandas Data Analysis tool, Artificial Intelligence, Machine Learning Algorithms, Deep Learning , Neural Networks, Natural Language Processing, Data Science training in real-time projects together with Data Science Placement training.

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Data Science

Data Science Training Course in Chennai – Why Troy Infotech

The Data Science training in Chennai is designed according to the latest industry trends and taking into account the content and advanced curriculum of the Data Science course based on the professional requirements of the student; helping them obtain a Position in multinational companies and achieve their professional goals.

TROY INFOTECH is the No. 1 Data Science training center in Chennai, with high-tech infrastructure and excellent lab facilities and students have the options to choose from multiple courses in Chennai Location.  Troy Infotech in Chennai prepares thousands of applicants for customized Data Science courses at reasonable prices, taking into account the training and content requirements of each and every student.

The Data Science course involves “Learning by doing”, using a state-of-the-art infrastructure to perform practical exercises and real-world simulations. This extensive practical experience in Data Science training ensures that you will absorb the knowledge and skills you will need to apply at work after being placed in an MNC.

There are numerous platforms and flows to build a product or application. When we talk about technologies and programming languages, Python is the chosen platform compared to R. It is used in the development of many applications like web development, game development, Machine learning, Artificial Intelligence, Data Science, Data Visualization, Web Scraping, Business Applications, Cad Applications, and more. Data Science Training in Chennai is the key to success.

About Data Science:

What is Data? 

Unprocessed information is Data. From Chaos is born, Clarity. Right? Every one of us process & sort data we have and convert it into meaningful information. It process happens all around us and encompasses our everyday activities.

What is Data Science?

Data Science has become the most sought after job now! Many young minds aspire to clinch a role as a Data Scientist.

Data Science is not a stand-alone field. It is a multi-disciplinary vocation that combines collection; construction; interpretation; conception and maintenance of data, on finding the best possible solution. It uses scientific methods and human aptitude to interpret structured and unstructured data. Statistical & Reasoning techniques are applied by studying machine learning and artificial intelligence like probability analysis, Predictive models and their applicability, are employed to achieve the desired results- Problem Solving!

Data Science, Deep Learning, Machine Learning, Artificial Learning are associated with one another and yet they are not parallels. New technologies are amplifying the scope and reach, thereby giving us unlimited access to obtain a meaningful perspective from immense data. Data Science applies machine learning algorithms by combining it with human judgment to predict optimized and reasonable outcomes.

Why is Data Science important? 

Data is the future of many industries and businesses. Businesses need relevant data to be analyzed to function efficiently and maintain the optimal progression rate. All companies have a vision they work toward achieving. In this juncture and millennia, data-driven strategies yield meaningful statistics, and therefore significant insights. We have to note that lack of accurate insights makes it difficult for the businesses to plan strategies that take years to develop, execute and reap fruition. Data Science is a reliable resource for any business that needs immense data to be processed. Data Science is generating outstanding results not just in Information & Technology, but in Healthcare as well. 

It is a rough estimate that by 2026 Data Science will have created approximately 11 million jobs across the globe.

Data Science and Problem Solving: 

Data Science is the domain where Data is the key and plays a major role. Data Science is primarily interpreting and understanding the information collected, making sense of the information which otherwise may seem utter chaos. This interpretation and perspicacity obtained are done by applying many methods including algorithms, statistics and several processes. It is an art by itself and an important skill Data Scientists possess is the ability to recognize a ‘Pattern’ in this chaotic data.  

Businesses these days operate from one part of the world, yet contributing to or serving people across continents. The ability to precisely predict the market development & changes in it and to be able to create & deliver an appropriate product or service is a challenge. To succeed in that requires enormous data analysis and an entire team with the primary focus of finding the accurate solution for the problem in hand. 

Problem-solving has risen to unexpected heights with the invention of several scientific methods and processes, including the automation of suitable algorithms. 

Scope of Data Science has reached a grand scale and is not limited to just ‘Problem-solving’. We don’t merely use Data Science to solve problems of enormous scale but Data Analysts can predict a collection of possible problems that may arise in the future and have available potential solutions defined and ready to apply, if and when needed. As such problems requiring immediate solutions as well as foreseeing problems of the future, both become possible because of Data Science. 

Influence of Data Science on our Future:

Data Science is an important and valuable resource for the future. Artificial Intelligence and Big Data bring exponential progress in terms of Data Analysis that can easily produce and deliver more reliable decisions and smarter products. In the past, we had people performing manual analysis of data that took a longer period to arrive at the decision. Statistics has always been the method that made this analysis possible and was called Qualitative Analysis. Now, we have automated and computerized the entire analysis method, with the support of multiple tools, suitable across various disciplines and fields. What we have now is an amalgamation of Computer Science and Statistics. 

Though most of the data that we talk about here is historical by nature, it has the potential to show and answer questions and solve problems involving customers and their needs and preferences. 

Future of technology is Artificial Intelligence and Data. They are likely to complement one another and grow stronger side by side rather than being adversaries. Data science is used in various fields from creating new customers, innovating new methods and new products, strong retailing solutions and above all adding value to people and their lives by making a priceless contribution to the medical science.

Troy Infotech recognizes that the future is dependent on computers, remote-model working and automated in multiple approaches. Our curriculum is based on our experience and an in-depth study of the industry’s trajectory, especially Information and Technology. 

Why should you invest and learn Data Science training in Chennai? 

Data Science is accelerating the growth of many fields. Data Scientists are sought after in many fields across multiple disciplines, especially industries specifically running on ‘Data’. 

Data Science is relatively new and yet has started showing growth as instantaneous as a bolt of lightning. It is estimated that the vacancies for Data Scientists are higher than the actual Data Scientists in the industry. This very reason makes this profession an attractive prospect. If there was ever a time to invest and prosper in Data Science, it is now! Data Scientists that can prove their worth and ability in problem-solving, quickly and precisely will be compensated generously. It must be remembered that though data is all around us in various forms, what the industry needs is someone that can make sense of the chaos and provide with polished data, and information worthy of application. This is possible only by people with experience handling data and understanding the same. Data Science is simply about the application of knowledge and understanding using various protocols and programming languages. For a Data Scientist to be successful they need to have strong operational knowledge & perception of these tools. It will allow them to formulate models and solution boards, and communicate the same precisely. Many companies these days depend on Data Scientists to make various, pressing and critical decisions. From health care to rocket science, Data plays an indispensable role. As such the job of a Data Scientist is lucrative, highly respected and cause-worthy.

With the entire business world turning to technology and remote working in the past few months, there has never been a greater advantage to Data Analytics Technology. 

Data Science- Skills: 

As a Data Scientist one is required to hold a set of skills that helps them utilize the tools available to analyze the abundant data!

These skills fall under 3 different operations- Analytics, Programming, and Domain Knowledge. 

The Data Science Skills that are considered a requirement:

  1. Statistics: Complex problems require Data Analysis to reach a solution. Data is collected, processed and analyzed using tools that support it. Statistics is significant and decisive in this analysis, as it represents facts from the past in definitive numbers that can help Data Scientists draw models. This eventually assists them to deliver accurate predictions based on the past data. 
  2. Critical thinking: Objectivity is a strong attribute that elevates the chances of making the right decisions on time, every time. Ability to make decisions based on facts and analysis, without allowing personal opinions is crucial and is possible when the Data Scientist has the ability to grasp only relevant data.
  3. Knowledge of AI, ML, and Deep Learning: Artificial Intelligence is going to influence more fields than we ever imagined it would. The possibilities are endless and it applies statistical methods to facilitate computers to make the necessary connections and study from the provided data. Machine Learning is a subset or component of Artificial Intelligence. It makes modelling and experimenting with self-driven cars, speech recognition, perception & study of the human genome, possible and successful.
  4. Programming skills: Data Scientist needs to possess impeccable skills when it comes to programming languages. Python, R and SQL knowledge helps the scientist conduct both statistical and computational slant effectively.
  5. Confident with Maths: Application of numbers in complex financial models and representations that are crucial leads Data Scientist to design pivotal strategies and business decisions.
  6. Good Knowledge of Python, R, SAS, and Scala: Data Scientists require working knowledge of Python, Scala, R and SAS. It increases self-dependency, thereby saving time and providing deeper clarity when it comes to performing tasks. 
  7. Data Wrangling: Data wrangling simply means the ability to work with a considerable amount of data that will be disarrayed and may necessitate refinement. Elimination of unwanted details, spotting missing strings, manipulating data until it offers clarity and insight is a skill the Data Scientists need.
  8. Data Visualization: Visualization is what sets Data Science apart from other technological developments. Especially when it comes to getting these visuals and graphics from an infinite amount of data. Visuals help Data Scientists converse and communicate their model-solutions to both technically sound people and non-technically inclined people.
  9. Ability to understand Analytical functions: Convergent power series is used locally for many Analytical functions and has infinitely many terms by definition. Extensive use of Differentiation and Integration transpires, and the ability to read; perform & understand the results is crucial to building models and predict future trends.
  10. Experience with SQL: SQL is a domain-specific language. It can handle data stored in the RDMS (Relational Database Management System). Stream Processing. It is applied to manage structured data in circumstances when data correlate with one another. 
  11. Ability to work with unstructured data: Data Science is a field that thrives on the availability of endless data. When it comes to sifting through this data, Data Scientists end up with unstructured data from various provenance & sources. It may include pictures, videos and social media. Wrangling data effortlessly will signify strength to the Data Scientists. It periodically includes pictures, videos and social media contents. Wrangling such data effortlessly will be strength to the Data Scientists.
  12. Communication: Communication is an invaluable asset for any professional. It connects people & bridges the gap that arises when personnel from diverse background and framework pool together working towards a common business assignment. Verbal and written communication helps Data Scientists as they engage with various vocations including business, technology and data analysis. Ability to aesthetically outline the message so that everyone involved follows the sometimes-complex models easily. This includes communicating the techniques chosen and applied to arrive at the prediction patterns.

Data Science for You: 

As a student or an engineer that aspires to become a Data Scientist, working towards gaining as much traction as possible in IT and Data Science, is natural & praiseworthy. 

Any subject knowledge that one acquires during their university education can only take halfway towards their dreams, especially in technology. We all know that the technology that is introduced today gets outdated in a few years. Though it is unfortunate news, it also means that there is accelerated growth on the technological front. This growth needs people to not just support businesses today, but predict the future of the product/business! This is where Data Science takes precedence over other studies. Its ability to abet predictions based on Data Analysis is conquering the business world. This is the appropriate moment in the millennia to invest and pursue Data Science.

Data Science Tools: 

Data Science is advanced in several disciplines that include Information technology, Agriculture, Healthcare and Finance. This multi-dimensional presence means that Data Science need multiple tools and rightly is loaded with the necessary tools.

These tools help the Data Scientists to seek insights by collecting, analyzing data and eventually offer in-depth detail to generate predictions. These predictions guide and drive the data scientists into designing appropriate strategies to reach their goals.

  1. SAS: SAS is a Data Science tool designed to perform Statistical Operations, specifically. SAS is closed-source proprietary software adapted by organizations significant in terms of size. It offers several libraries and sub-tools for modelling & organizing the acquired data. SAS is remarkably reliable and expensive. For this reason, only large entities employ it.
  2. Apache Spark: Apache Spark is the most used Data Science Tool. It is an all-round, robust analytical engine. It is an extremely fast-performing tool and is nearly 100 times quicker than MapReduce. Apache Spark boasts of having multiple Machine Learning APIs that assists Data Scientists to construct accurate predictions. It is capable of delivering reliable results than Big Data Platforms while handling data streaming. It is more efficient than Hadoop in cluster management and this enables Spark to process application at an extraordinary pace. Spark extends APIs configurable in Python, R and Java.
  3. BigML: BigML is a broadly adopted Data Science Tool that offers a thoroughly interact able, Cloud-based GUI environment which is used for processing Machine Learning Algorithms. It offers regulated software using Cloud Computing for business needs and also comes with several automation methods including automation of reusable scripts. BigML excels in predictive modelling and renders a lenient web-interface using Rest APIs. Ability to generate visualizations of data and also exporting them to the device of our choice (IoT devices or Mobile).
  4. D3.js: D3.js uses Javascript as a client-side script language. Interactive Visualizations is possible via Javascript library, on the web browser. By using multiple APIs D3.js enables numerous functional logs creating compelling image. Animated transactions are possible in D3.js, as well. Clients will be able to execute changes and updates, thereby making the documents versatile. Using D3.js along with CSS benefits clients build illustrations and fleeting visualizations, customizing them. Especially useful when working on IoT devices.
  5. MATLAB: MATLAB is a complete analytical computing environment for processing mathematical data. It is closed-source software that offers algorithmic application, matrix functions and statistical data modelling. It is a versatile tool where Image & Signal processing is also possible. Automation of tasks is candid because of this versatile feature.
  6. Excel: Most definitely the most universally used Data Analysis Tool. Though Microsoft developed this for calculations, it is not widely used to perform data processing, visualization and laborious calculations. Excel can be connected with SQL to make it more dynamic and dexterous. It is regularly used by Data Scientists for data cleansing to process information effortlessly. Toolpac has now made it even simpler to analyze complicated data.
  7. ggplt2: This tool was created to replace the graphic package of R. It is an upper-level data visualization tool. Powerful commands are used to produce distinguished visualizations. It offers customization and is aesthetically pleasing. Glossary of data in the visuals by labeling is possible in ggplt2. It is also one of the most used Data Science tools.
  8. Tableau: Tableau is a Data Visualization Software with outstanding graphics that delivers interactive visualizations. It can execute brilliant graphics by documenting hundreds of data reports. Tableau can collaborate with databases, spreadsheets, OLAP (Online Analytical Processing) cubes, etc. Another feature unique to Tableau is its resourcefulness to plot longitudes and latitudes.
  9. Jupyter: It is an open-source tool that helps developers make open-source software. It is based on Ipython and supports several languages like Julia, Python and R. It is an interactive and influential tool for storytelling with various presentation features. It is a web-application widget used for writing live codes and presentations.
  10. Matplotlib: Matplotlib- developed by Python, is a plotting and visualization library. It is applied for plotting complex graphs, employing simple lines of codes. Histograms, bar plots, scatterplots are a few graphs that can be generated from Matplotlib. Pyplot is its broadly used module It is an open-source substitute to MATLAB’s graphic modules.
  11. NLTK: Natural Language Processing has surfaced as the most sought after field in Data Science. Computers understanding and interpreting human languages is no more an illusion, as this tool deals with the aforementioned developmental model. Python comes equipped with a collection in its library called Natural Language Tool Kit- NLTK. It comprises of several applications like Parts of Speech, Word Segmentation, Machine Translation, and Test to Speech Recognition etc.
  12. Scikit-learn: Scikit-learn are another simple and easy to implement tool from Python. It makes using complex machine learning algorithms, easy. It is a perfect fit for situations requiring accelerated prototyping. This tool utilizes numerous fundamental libraries of Python like Numpy, SciPy, Matplotlip, etc.
  13. TensorFlow: TensorFlow is an open-source tool & is named after Tensors that are multi-dimensional arrays. It is broadly used for exceptional machine learning algorithms like Deep Learning. It is a constantly evolving toolkit that is well received for its performance and extraordinary computational ingenuity. TensorFlow is capable of running on both CPUs and GPUs and lately is catching up on TPUs as well. This gives TensorFlow an unparalleled advantage. The variability of applications includes speech recognition, drug development, image analysis, image and language recognition, etc. It is a necessary tool for Data Scientists training in Machine Learning.
  14. Weka: Waikato Environment for Knowledge Analysis is Machine Learning software. It is written in Java and is a compilation of several algorithms for Data mining. It is an open-source GUI software that enables simpler implementation through the interactable platform. This tool is ideal for Data Scientists beginning to train in Machine Learning.

Math and Statistics for Data Science:

Data Science is the manipulation of Data. It is changing the face of cluttered data into organized and meaningful information calls for intelligence and understanding. 

Data Analysis requires automated tools that employ disciplines from Maths and Statistics. Hence it is an absolute advantage and necessity to have a strong grasp of Mathematical and statistical concepts. Mathematics is intertwined in all fields, including Data Science. Two of its concepts, namely Linear Algebra and Calculus, provide an immeasurable contribution to Data Science. Likewise, concepts like Skewness & Kurtosis, Central Tendency, Normal Distribution, Variability, Hypothesis testing, Central Limit Theorem, Correlation& Regression (Qualitative Data Analysis) relating to Statistics also offers greater augmentation to Data Science.

Data Science for Business: Data Science is being used by all businesses in various capacities making a variety of decisions. Gathering data and analyzing them to decide the next new product to launch for tapping the soon-to-be-trending market, to investigate the market to know the right time to launch a product, to marketing solutions, etc. Decision making has to be wise, smart, precise and timely. Strategies developed based on the data analysis have to be modelled clearly to avoid ambiguous knowledge transfer. Data Analysis makes data-driven decisions not just a possibility, but accurate and successful. Every past decision can be analyzed and necessary amends made in the future decisions, avoiding the patterns that failed, to achieve the expected outcome. 

  • DATA SCIENTIST JOB ROLE
  • COMPANIES RECRUITING DATA SCIENTIST

DATA SCIENTISTS JOB ROLE AND THEIR SALARIES

JUNIOR DATA ENGINEER – 1.2L TO 1.8 L

DATA SCIENTIST – 1.8L TO 2.4L

SENIOR DATA ENGINEER  – 4L TO 6L

SENIOR DATA SCIENTIST – 5L TO 7L

DATA SCIENTIST TEAM LEAD – 6L TO 8L

DATA SCIENCE MANAGER – 8L TO 12L

LEARN Data Science Training in Chennai @ Troy Infotech and get placed in one of the MNC's:

  • CTS
  • TCS
  • Accenture
  • IBM
  • Infosys
  • Amazon
  • Capgemini
  • Wipro
  • Tech Mahindra
  • Syntos
  • HCL Technologies
  • CGI
  • Computer Science Corporation (CSC)
  • GOFRUGAL Technologies Pvt Ltd
  • Fabsys Technologies Private Limited
  • Newt Global
  • Fabhost Web Solution
  • Auromine Solutions Private Limited
  • Neptune Financial Software
  • FinaTel Technologies
  • Talent Wagon
  • Techpoint
  • JP Morgan Chase & Co
  • Why Troy Infotech
  • About Data Science
  • Certification
  • Data Science Tools

  About Data Science:

What is Data? 

Unprocessed information is Data. From Chaos is born, Clarity. Right? Every one of us process & sort data we have and convert it into meaningful information. It process happens all around us and encompasses our everyday activities.

What is Data Science?

Data Science has become the most sought after job now! Many young minds aspire to clinch a role as a Data Scientist.

Data Science is not a stand-alone field. It is a multi-disciplinary vocation that combines collection; construction; interpretation; conception and maintenance of data, on finding the best possible solution. It uses scientific methods and human aptitude to interpret structured and unstructured data. Statistical & Reasoning techniques are applied by studying machine learning and artificial intelligence like probability analysis, Predictive models and their applicability, are employed to achieve the desired results- Problem Solving!

Data Science, Deep Learning, Machine Learning, Artificial Learning are associated with one another and yet they are not parallels. New technologies are amplifying the scope and reach, thereby giving us unlimited access to obtain a meaningful perspective from immense data. Data Science applies machine learning algorithms by combining it with human judgment to predict optimized and reasonable outcomes.

Why is Data Science important? 

Data is the future of many industries and businesses. Businesses need relevant data to be analyzed to function efficiently and maintain the optimal progression rate. All companies have a vision they work toward achieving. In this juncture and millennia, data-driven strategies yield meaningful statistics, and therefore significant insights. We have to note that lack of accurate insights makes it difficult for the businesses to plan strategies that take years to develop, execute and reap fruition. Data Science is a reliable resource for any business that needs immense data to be processed. Data Science is generating outstanding results not just in Information & Technology, but in Healthcare as well. 

It is a rough estimate that by 2026 Data Science will have created approximately 11 million jobs across the globe.

Data Science and Problem Solving: 

Data Science is the domain where Data is the key and plays a major role. Data Science is primarily interpreting and understanding the information collected, making sense of the information which otherwise may seem utter chaos. This interpretation and perspicacity obtained are done by applying many methods including algorithms, statistics and several processes. It is an art by itself and an important skill Data Scientists possess is the ability to recognize a ‘Pattern’ in this chaotic data.  

Businesses these days operate from one part of the world, yet contributing to or serving people across continents. The ability to precisely predict the market development & changes in it and to be able to create & deliver an appropriate product or service is a challenge. To succeed in that requires enormous data analysis and an entire team with the primary focus of finding the accurate solution for the problem in hand. 

Problem-solving has risen to unexpected heights with the invention of several scientific methods and processes, including the automation of suitable algorithms. 

Scope of Data Science has reached a grand scale and is not limited to just ‘Problem-solving’. We don’t merely use Data Science to solve problems of enormous scale but Data Analysts can predict a collection of possible problems that may arise in the future and have available potential solutions defined and ready to apply, if and when needed. As such problems requiring immediate solutions as well as foreseeing problems of the future, both become possible because of Data Science. 

  About Data Science:

What is Data? 

Unprocessed information is Data. From Chaos is born, Clarity. Right? Every one of us process & sort data we have and convert it into meaningful information. It process happens all around us and encompasses our everyday activities.

What is Data Science?

Data Science has become the most sought after job now! Many young minds aspire to clinch a role as a Data Scientist.

Data Science is not a stand-alone field. It is a multi-disciplinary vocation that combines collection; construction; interpretation; conception and maintenance of data, on finding the best possible solution. It uses scientific methods and human aptitude to interpret structured and unstructured data. Statistical & Reasoning techniques are applied by studying machine learning and artificial intelligence like probability analysis, Predictive models and their applicability, are employed to achieve the desired results- Problem Solving!

Data Science, Deep Learning, Machine Learning, Artificial Learning are associated with one another and yet they are not parallels. New technologies are amplifying the scope and reach, thereby giving us unlimited access to obtain a meaningful perspective from immense data. Data Science applies machine learning algorithms by combining it with human judgment to predict optimized and reasonable outcomes.

Why is Data Science important? 

Data is the future of many industries and businesses. Businesses need relevant data to be analyzed to function efficiently and maintain the optimal progression rate. All companies have a vision they work toward achieving. In this juncture and millennia, data-driven strategies yield meaningful statistics, and therefore significant insights. We have to note that lack of accurate insights makes it difficult for the businesses to plan strategies that take years to develop, execute and reap fruition. Data Science is a reliable resource for any business that needs immense data to be processed. Data Science is generating outstanding results not just in Information & Technology, but in Healthcare as well. 

It is a rough estimate that by 2026 Data Science will have created approximately 11 million jobs across the globe.

Data Science and Problem Solving: 

Data Science is the domain where Data is the key and plays a major role. Data Science is primarily interpreting and understanding the information collected, making sense of the information which otherwise may seem utter chaos. This interpretation and perspicacity obtained are done by applying many methods including algorithms, statistics and several processes. It is an art by itself and an important skill Data Scientists possess is the ability to recognize a ‘Pattern’ in this chaotic data.  

Businesses these days operate from one part of the world, yet contributing to or serving people across continents. The ability to precisely predict the market development & changes in it and to be able to create & deliver an appropriate product or service is a challenge. To succeed in that requires enormous data analysis and an entire team with the primary focus of finding the accurate solution for the problem in hand. 

Problem-solving has risen to unexpected heights with the invention of several scientific methods and processes, including the automation of suitable algorithms. 

Scope of Data Science has reached a grand scale and is not limited to just ‘Problem-solving’. We don’t merely use Data Science to solve problems of enormous scale but Data Analysts can predict a collection of possible problems that may arise in the future and have available potential solutions defined and ready to apply, if and when needed. As such problems requiring immediate solutions as well as foreseeing problems of the future, both become possible because of Data Science. 

  About Data Science:

What is Data? 

Unprocessed information is Data. From Chaos is born, Clarity. Right? Every one of us process & sort data we have and convert it into meaningful information. It process happens all around us and encompasses our everyday activities.

What is Data Science?

Data Science has become the most sought after job now! Many young minds aspire to clinch a role as a Data Scientist.

Data Science is not a stand-alone field. It is a multi-disciplinary vocation that combines collection; construction; interpretation; conception and maintenance of data, on finding the best possible solution. It uses scientific methods and human aptitude to interpret structured and unstructured data. Statistical & Reasoning techniques are applied by studying machine learning and artificial intelligence like probability analysis, Predictive models and their applicability, are employed to achieve the desired results- Problem Solving!

Data Science, Deep Learning, Machine Learning, Artificial Learning are associated with one another and yet they are not parallels. New technologies are amplifying the scope and reach, thereby giving us unlimited access to obtain a meaningful perspective from immense data. Data Science applies machine learning algorithms by combining it with human judgment to predict optimized and reasonable outcomes.

Why is Data Science important? 

Data is the future of many industries and businesses. Businesses need relevant data to be analyzed to function efficiently and maintain the optimal progression rate. All companies have a vision they work toward achieving. In this juncture and millennia, data-driven strategies yield meaningful statistics, and therefore significant insights. We have to note that lack of accurate insights makes it difficult for the businesses to plan strategies that take years to develop, execute and reap fruition. Data Science is a reliable resource for any business that needs immense data to be processed. Data Science is generating outstanding results not just in Information & Technology, but in Healthcare as well. 

It is a rough estimate that by 2026 Data Science will have created approximately 11 million jobs across the globe.

Data Science and Problem Solving: 

Data Science is the domain where Data is the key and plays a major role. Data Science is primarily interpreting and understanding the information collected, making sense of the information which otherwise may seem utter chaos. This interpretation and perspicacity obtained are done by applying many methods including algorithms, statistics and several processes. It is an art by itself and an important skill Data Scientists possess is the ability to recognize a ‘Pattern’ in this chaotic data.  

Businesses these days operate from one part of the world, yet contributing to or serving people across continents. The ability to precisely predict the market development & changes in it and to be able to create & deliver an appropriate product or service is a challenge. To succeed in that requires enormous data analysis and an entire team with the primary focus of finding the accurate solution for the problem in hand. 

Problem-solving has risen to unexpected heights with the invention of several scientific methods and processes, including the automation of suitable algorithms. 

Scope of Data Science has reached a grand scale and is not limited to just ‘Problem-solving’. We don’t merely use Data Science to solve problems of enormous scale but Data Analysts can predict a collection of possible problems that may arise in the future and have available potential solutions defined and ready to apply, if and when needed. As such problems requiring immediate solutions as well as foreseeing problems of the future, both become possible because of Data Science. 

Data Science Tools: 

Data Science is advanced in several disciplines that include Information technology, Agriculture, Healthcare and Finance. This multi-dimensional presence means that Data Science need multiple tools and rightly is loaded with the necessary tools.

These tools help the Data Scientists to seek insights by collecting, analyzing data and eventually offer in-depth detail to generate predictions. These predictions guide and drive the data scientists into designing appropriate strategies to reach their goals.

  1. SAS: SAS is a Data Science tool designed to perform Statistical Operations, specifically. SAS is closed-source proprietary software adapted by organizations significant in terms of size. It offers several libraries and sub-tools for modelling & organizing the acquired data. SAS is remarkably reliable and expensive. For this reason, only large entities employ it.
  2. Apache Spark: Apache Spark is the most used Data Science Tool. It is an all-round, robust analytical engine. It is an extremely fast-performing tool and is nearly 100 times quicker than MapReduce. Apache Spark boasts of having multiple Machine Learning APIs that assists Data Scientists to construct accurate predictions. It is capable of delivering reliable results than Big Data Platforms while handling data streaming. It is more efficient than Hadoop in cluster management and this enables Spark to process application at an extraordinary pace. Spark extends APIs configurable in Python, R and Java.
  3. BigML: BigML is a broadly adopted Data Science Tool that offers a thoroughly interact able, Cloud-based GUI environment which is used for processing Machine Learning Algorithms. It offers regulated software using Cloud Computing for business needs and also comes with several automation methods including automation of reusable scripts. BigML excels in predictive modelling and renders a lenient web-interface using Rest APIs. Ability to generate visualizations of data and also exporting them to the device of our choice (IoT devices or Mobile).
  4. D3.js: D3.js uses Javascript as a client-side script language. Interactive Visualizations is possible via Javascript library, on the web browser. By using multiple APIs D3.js enables numerous functional logs creating compelling image. Animated transactions are possible in D3.js, as well. Clients will be able to execute changes and updates, thereby making the documents versatile. Using D3.js along with CSS benefits clients build illustrations and fleeting visualizations, customizing them. Especially useful when working on IoT devices.
  5. MATLAB: MATLAB is a complete analytical computing environment for processing mathematical data. It is closed-source software that offers algorithmic application, matrix functions and statistical data modelling. It is a versatile tool where Image & Signal processing is also possible. Automation of tasks is candid because of this versatile feature.
  6. Excel: Most definitely the most universally used Data Analysis Tool. Though Microsoft developed this for calculations, it is not widely used to perform data processing, visualization and laborious calculations. Excel can be connected with SQL to make it more dynamic and dexterous. It is regularly used by Data Scientists for data cleansing to process information effortlessly. Toolpac has now made it even simpler to analyze complicated data.
  7. ggplt2: This tool was created to replace the graphic package of R. It is an upper-level data visualization tool. Powerful commands are used to produce distinguished visualizations. It offers customization and is aesthetically pleasing. Glossary of data in the visuals by labeling is possible in ggplt2. It is also one of the most used Data Science tools.
  8. Tableau: Tableau is a Data Visualization Software with outstanding graphics that delivers interactive visualizations. It can execute brilliant graphics by documenting hundreds of data reports. Tableau can collaborate with databases, spreadsheets, OLAP (Online Analytical Processing) cubes, etc. Another feature unique to Tableau is its resourcefulness to plot longitudes and latitudes.
  9. Jupyter: It is an open-source tool that helps developers make open-source software. It is based on Ipython and supports several languages like Julia, Python and R. It is an interactive and influential tool for storytelling with various presentation features. It is a web-application widget used for writing live codes and presentations.
  10. Matplotlib: Matplotlib- developed by Python, is a plotting and visualization library. It is applied for plotting complex graphs, employing simple lines of codes. Histograms, bar plots, scatterplots are a few graphs that can be generated from Matplotlib. Pyplot is its broadly used module It is an open-source substitute to MATLAB’s graphic modules.
  11. NLTK: Natural Language Processing has surfaced as the most sought after field in Data Science. Computers understanding and interpreting human languages is no more an illusion, as this tool deals with the aforementioned developmental model. Python comes equipped with a collection in its library called Natural Language Tool Kit- NLTK. It comprises of several applications like Parts of Speech, Word Segmentation, Machine Translation, and Test to Speech Recognition etc.
  12. Scikit-learn: Scikit-learn are another simple and easy to implement tool from Python. It makes using complex machine learning algorithms, easy. It is a perfect fit for situations requiring accelerated prototyping. This tool utilizes numerous fundamental libraries of Python like Numpy, SciPy, Matplotlip, etc.
  13. TensorFlow: TensorFlow is an open-source tool & is named after Tensors that are multi-dimensional arrays. It is broadly used for exceptional machine learning algorithms like Deep Learning. It is a constantly evolving toolkit that is well received for its performance and extraordinary computational ingenuity. TensorFlow is capable of running on both CPUs and GPUs and lately is catching up on TPUs as well. This gives TensorFlow an unparalleled advantage. The variability of applications includes speech recognition, drug development, image analysis, image and language recognition, etc. It is a necessary tool for Data Scientists training in Machine Learning.
  14. Weka: Waikato Environment for Knowledge Analysis is Machine Learning software. It is written in Java and is a compilation of several algorithms for Data mining. It is an open-source GUI software that enables simpler implementation through the interactable platform. This tool is ideal for Data Scientists beginning to train in Machine Learning.

Math and Statistics for Data Science:

Data Science is the manipulation of Data. It is changing the face of cluttered data into organized and meaningful information calls for intelligence and understanding. 

Data Analysis requires automated tools that employ disciplines from Maths and Statistics. Hence it is an absolute advantage and necessity to have a strong grasp of Mathematical and statistical concepts. Mathematics is intertwined in all fields, including Data Science. Two of its concepts, namely Linear Algebra and Calculus, provide an immeasurable contribution to Data Science. Likewise, concepts like Skewness & Kurtosis, Central Tendency, Normal Distribution, Variability, Hypothesis testing, Central Limit Theorem, Correlation& Regression (Qualitative Data Analysis) relating to Statistics also offers greater augmentation to Data Science.

 

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  • Software developers
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  • Python is already used by most of the top industries across various business verticals.
  • Artificial Intelligence is changing the world, where Python plays an important role to have a bright future & having more job opportunities in MNC’s.
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Python is the easiest Programming Language and its quite easy to learn, Its syntax is easy and simple to understand. 

As Python the Fastest growing language,  has a promising future in the software industry

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