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Data Science ( DS ) - An Emerging Computer Science Discipline - Introduction & Scope :
Data Science ( DS ) is an important specialisation born out of the discipline of Computer Science. It has a huge scope for industrial applications. Data Science appears to have significant emerging potential for the future of ever evolving Probabilistic Distributions, Accounting Analysis, Statistics, Scientific Research Computations, BioTechnology, BioInformatics, Mass Media Applications MMS, Cyber Security Systems, Machine Learning, Genomes Analysis, Applied Maths, Financial Analysis, Physics, Genetics Engineering, Chemistry, Historical Data Archiving, Web Applications, Medical Sciences, English & Other Languages Archivals, Biological Analysis, Neural Networks, Deep Learning, Decision Support Systems, Multimedia Systems, Marketing Research, Geospatial Technology & Engineering, Distributed Databases, Informatics Practices IP, Business Intelligence Visualisations, Typography & Computer Applications TCA, Distributed Operating Systems, Information & Communication Technology ICT, Development of Coding & Programming Languages, Operations Research, Computers & Communication Technology CCT, Computing Algorithms, Information Technology IT Enabled Systems, Library Information Systems LIS, Big Data Oriented ( Data Gathering, Data Classification, Data Analysis, Data Processing & Data Interpretations ), Data Association Principles and related applications worldwide. Through the analytical outcome of Data Science, the world wide web, i.e., "www", gets better leveraged to be connected globally for meeting various integrations and implementations over varying data formats. All this assists in sustaining the contemporary human systems and structures in various ways, including Digital Transformations, Automations, Format Alignments and others. Data Science by encompassing effective communication propagation paradigm across evolving Human Structures & Systems, including Historical Constructs, Geographical Constructs, Traditional constructs, Cultural constructs, Social Science constructs, Sociology constructs, Political Science constructs, Legal Science constructs, Economics constructs, Psychology constructs, enables overcoming various communication barriers, as well as smoothens the process of communication dissemination.
Data Science ( DS ) is leveraged for achieving better and informed data decision making across all the industrial domains, including but not limited to finance, healthcare, automations, automotive, metal recycling, administration, banking, pharmacies and whatnots. Data Science, technically speaking, is a thorough examination of the vast amounts of data. Data Science entails deriving scientifically valid conclusions from raw data that may be structured, semi structured and unstructured in nature. In Data Science this raw data is processed using various tools, algorithms, and technologies to extract intelligent patterns, as well as meaningful information. It is an interdisciplinary discipline that use tools and methods to modify the data in order to discover something or some patterns that are unique and significant. Data science employs the most advanced technology, coding languages, and algorithms to address data-related issues. It is the upcoming future of the field of AI or artificial intelligence.
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Briefly stated, data science is all about posing the right inquiries, reviewing the raw data, employing a variety of sophisticated and effective algorithms to model the data, visualizing the facts to have a clearer understanding, understanding the facts to reach the end result and making better judgments. Learning data science gives you the chance to discover a variety of intriguing work possibilities in this industry. The following is a list of major employment roles that go along with this discipline of Data Science, viz., Data Scientist, Data Analyst, Machine learning expert, Data engineer, Data Architect, Data Administrator, Business Analyst and Business Intelligence Manager. There are further interactions and interfacing with many other stakeholders, including the end users, Business Teams, Development teams, Engineering teams, infrastructure teams, Finance teams, operations teams, marketing teams and almost all the Business stakeholders.
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Following is the general CBSE / NCERT prescribed outline of the syllabus for subject Data Science ( DS ) - Class 8th ( Class VIII - 8 ) :
Introduction to Data
1. What is Data?
2. Real-World Examples of Data
Introduction to Data Science
1. A brief introduction to Data Science
2. Careers in Data science
3. What does Data Science help us achieve?
2. What is data visualization?
3. Examples of data visualization
4. Importance of data visualization
5. Asking the right question
Data Science and AI
2. Applications of data science
3. Analytics on text data
4. Analytics on image data
5. Overview of AI
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Following is the general CBSE / NCERT prescribed outline of the syllabus for subject Data Science ( DS ) - Class 9th ( Class IX - 9 ) :
1. What is data ?
2. Data vs. Information
3. Data Information Knowledge Wisdom ( DIKW model )
4. How data influence our lives ?
5. What are data footprints ?
6. Data Loss and recovery
Arranging and Collecting Data
2. What is data collection ?
4. Types of data
5. Sources of data
6. What is Big Data ?
7. Questioning your data
8. Univariate and multivariate data
2. Importance of Data Visualisation
3. Plotting data
5. Use of shapes
6. Use of Single and Multi Variable plots
Ethics In Data Science
2. Ethical guidelines around data analysis
3. Need for ethical guidelines
4. Goals of ethical guidelines
5. Data governance framework
6. Need to govern data
7. Goals of data governance
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Following is the general CBSE / NCERT prescribed outline of the syllabus for subject Data Science ( DS ) - Class 10th ( Class X - 10 ) :
USE OF STATISTICS IN DATA SCIENCE
1. Lesson Structure
2. Lesson Plan
4. What are subsets?
5. Two-way frequency table
6. Interpreting two-way tables
7. Two-way relative frequency table
8. Meaning of mean
10. Mean Absolute Deviation
11. What is Standard Deviation?
DISTRIBUTIONS IN DATA SCIENCE
1. Lesson Structure
2. Lesson Plan
4. What is distribution in data science?
5. What are different types of distributions?
6. Statistical Problem Solving Process
7. Activity – Choosing groups for school dance program
1. Lesson Structure
2. Lesson Plan
3. What is partiality, preference and prejudice?
4. How to identify the partiality, preference and prejudice?
5. Probability for Statistics
6. The Central Limit Theorem
7. Why is the Central Limit Theorem important?
1. Lesson Structure
2. Lesson Plan
3. Overview of Data Merging
4. What is Z-Score?
5. How to calculate a Z-score?
6. How to interpret the Z-score?
7. Why is a Z-score so important?
8. Concept of Percentiles
ETHICS IN DATA SCIENCE
1. Lesson Structure
2. Lesson Plan
3. Note about data governance framework
4. Ethical guidelines around data analysis
5. Discarding the Data
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Data Science requires certain technical pre - requisites to be met before actually embarking upon the journey of implementation and its related applications. The first pre-requisite deals with concept of Machine Learning. To grasp the notion of data science, one must first grasp the concept of machine learning. Machine learning algorithms are used in data science to tackle various challenges. Secondly, modelling in terms of the mathematical equations is another critical pre-requisite. Mathematical modelling is necessary to do quick mathematical computations and predictions based on available data. Third pre-requisite revolves around the discipline of Statistics. It is necessary to have a fundamental grasp of statistics, including concepts like mean, median, and standard deviation. It is required to take information from the data and produce better outcomes. Fourth major pre-requisite is that of Computer Programming. Data scientists must be familiar with at least one programming language. Some of the computer programming languages needed for data science are R, Python, and Spark. Fifth significant pre-requisite is of Databases. Data science requires a thorough grasp of databases, such as SQL, in order to get and deal with data.
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Following is the general CBSE / NCERT prescribed outline of the syllabus for subject Data Science ( DS ) - Class 11th ( Class XI - 11 ) :
ETHICS IN DATA SCIENCE
2. How Data Ecosystem is evolving
3. Why do Data Scientists need to understand ethics?
4. What is data governance framework?
2. Story vs. facts
3. Trial assessment
FORECASTING ON DATA
3. Observational study
2. Let us do a survey
3. Sampling Bias
4. How sure are you?
5. Let us act on a sense
6. Online Data
7. Charm of XML
INTRODUCTION TO R STUDIO
2. Orientation with R Studio
3. Coding for Data Science using R-Studio
4. Code examples with R-Studio
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Data was less abundant and largely available in organised form a few years ago, which could be readily saved in excel sheets and analysed using BI tools. However, in today's world, data is getting so large that around 2.5 quintillion bytes of data are generated every day, resulting in data explosion. According to studies, by 2020, a single individual on Earth would generate 1.7 MB of data every single second. Every business requires data to function, develop, and improve. Handling such massive amounts of data is now a difficult undertaking for any firm. So, in order to manage, process, and analyse this, we needed some complicated, powerful, and efficient algorithms and technology, and that technology was developed in the form of Data Science.
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The following are some of the primary reasons for utilising data science technology. We can translate enormous amounts of raw and unstructured data into relevant insights using data science technologies. Data science technology is being adopted by a variety of businesses, whether large or small. Most customer oriented firms use data science algorithms to improve user experience. Data science is striving to automate transportation, such as constructing a self-driving car, which is the future of transportation. Data science may aid in numerous forecasts such as surveys, elections, and travel ticket confirmation, among others. Following are some of the major tools required for data science. First Ones are the Data Analysis Tools like R, Python, Statistics, SAS, Jupyter, R Studio, MATLAB, Excel and RapidMiner. Second ones are the Data Warehousing tools such as ETL, SQL, Hadoop, Informatica / Talend and AWS Redshift. Third ones are the Data Visualization tools like R, Jupyter, Tableau and Cognos. Fourth ones are the Machine learning tools like Spark, Mahout and Azure ML studio.
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Following is the general CBSE / NCERT prescribed outline of the syllabus for subject Data Science ( DS ) - Class 12th ( Class XII - 12 ) :
1. What is Data Governance?
2. Ethical Guidelines
3. Data Privacy
Exploratory Data Analysis
2. Univariate Analysis
3. Multivariate Analysis
4. Data Cleaning
Classification Algorithms I
2. Introduction to Decision Trees
3. Applications of Decision Trees
4. Creating a Decision Tree
Classification Algorithms II
2. Introduction to K-Nearest Neighbors
3. Pros and Cons of using K-NN
Regression Algorithms I
2. Introduction to Linear Regression
3. Mean Absolute Error
4. Root Mean Square Deviation
Regression Algorithms II
2. Multiple Linear Regression
3. Non-linear Regression
2. Introduction to Unsupervised Learning
3. Real-world applications of Unsupervised Learning
4. Introduction to Clustering
5. K - Means Clustering
Final Project I
2. Introduction to the Project
3. Setup Visual Studio Code and Python
4. Gather data for the meteor showers
5. Cleanse meteor data
6. Write the predictor function
Final Project II
2. Introduction to the Project
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To take up the subject of Data Science we also require certain mindset and higher order thinking skills. Following are a few of the Non Technical Pre-requisites required to embark upon the lessons of Data Science. First one is the presence of Curiosity in the Data Science Learner and explorer. In order to study data science, one must be curious. One can quickly comprehend a company challenge and ask many related inquiries. Second one is the presence of Critical thinking. It is also essential for a data scientist to uncover different efficient methods feasible to tackle an issue. Third ones are the Communication skills. These are essential for a data scientist since after addressing a business problem, one needs to convey it to the concerned team.
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In order to become a data scientist, one needs also be familiar with machine learning and its algorithms. This is because many machine learning algorithms are employed in data science. These Data Science algorithms could be classified under Supervised and Unsupervised Algorithms. The names of a few machine learning algorithms used in data science are as follows, Regression algorithms, Decision tree algorithms, Clustering algorithms, Principal component analysis algorithms, Support vector machines algorithms, Naive Bayes algorithms, Artificial neural network algorithms and Apriori algorithms. All these algorithms are used widely within the domain of Data Science. These are utilised under different conditions and are meant to achieve differing objectives.
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