Unit V & VI Maths, DI, and Reasoning Crash Course – Quick Revision (Terms & Concepts) Based on New Paper 1 Syllabus for 2020

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Crash Course – Quick Revision (Terms & Concepts)

Syllabus

Unit-V Mathematical Reasoning and Aptitude

Types of reasoning. Number series, Letter series, Codes and Relationships. Mathematical Aptitude (Fraction, Time & Distance, Ratio, Proportion and Percentage, Profit and Loss, Interest and Discounting, Averages etc.).

Unit-VII Data Interpretation

Sources, acquisition and classification of Data. Quantitative and Qualitative Data. Graphical representation (Bar-chart, Histograms, Pie-chart, Table-chart and Line-chart) and mapping of Data. Data Interpretation. Data and Governance

Maths

Arithmetic

Number Series

Rules of Divisibility

Fractions

HCF LCM

Ratio and Proportion

Binary to Decimal

Sets and Venn

Number Series

Picture based

Picture based

Picture Based

Picture based

Expressions

Equations

Income – Expenditure

Profit – Loss

Discounts – Partnerships

Interest Discounts

Abstract spatial reasoning

Cube and dice

3D problems

Time, Speed and Distance

Problem on Unitary Methods

Problem on Age

Problem on Mixture

Calendar and Clocks

Temperature

Data Sufficiency

Table

Pie Chart

Line Graph

Bar Graph

Basics

Data Governance

Data governance

Data Governance

Data governance

  • Data Governance Institute states that “Data Governance is a system of decision-rights and accountability for information-related processes, executed according to agreed-upon models that describe who can take what actions with what information, and when, under what circumstances, using what methods.”

  • 3 pillars – people, process and technology

Aspects of Data Governance

Levels of Data Governance – Operational, tactical, strategic

4 Pillars

Regulatory Requirements

Data governance requires an organization to understand regulatory requirements and business best practices. Their data must meet established rules and adopt automated and human processes to see that the rules are enforced.

Data Security

  • Data security prevents sensitive information from getting into the wrong hands. Regulated industries, such as healthcare or banking, have the most at stake. The amount of effort and expense spent on data security should be commensurate with the amount of risk.

  • Governance dictates where data may be stored and codifies data protection methods, such as encryption or password strength. Governance can dictate how to back up data, who has access to data, and when archived data should be destroyed.

Quality

Quality is a mandatory piece of a larger governance strategy. Without it, the organization is not going to successfully manage and govern the data.

4 Pillars –

  • Data Stewardship: Data Stewards implement the policies in an organization. They are accountable for data quality in terms of accuracy, accessibility, consistency, completeness, and updating. Teams of data stewards guide actual data governance implementations and may include database administrators, business analysts, and business personnel familiar with specific aspects of data within the organization. Data stewards ensure data use conforms to data governance policies.

  • Data Quality: Accuracy, completeness, and consistency across the data sources are hallmarks of successful initiatives. Here we have some specific terms associated with data quality as:

    • Data scrubbing, also known as data cleansing, is a common element in data quality which identifies, correlates and removes duplicated instances same data. Data scrubbing accounts for the ways in which, for example, the same customer or product may be described.

    • Data editors, data mining tools, as well as version control, workflow and project management systems are included among software types that help organizations attain better data quality.

  • Master Data Management: It establishes a master reference to ensure consistent use of data across large organizations. Metadata repositories, which hold data about data, establish data consistency across groups in an organization.

  • Use Cases

Aspects of Data Governance

Levels of Data Governance – Operational, tactical, strategic

Principles – transparency, auditability

Benefits

Big Data

Algorithms

Analytics

Descriptive Analytics

Predictive Analytics

Prescriptive Analytics

  • DAMA International: DAMA stands for Data Management Association. It is a not-for-profit, vendor-independent, international association of technical, and business professionals dedicated to advancing the concepts and practices of information resource management (IRM) and data resource management (DRM).

  • Data Governance Professionals Organization or DGPO: Non-profit, vendor neutral, association of business, IT and data professionals dedicated to advancing the discipline of data governance. It fosters discussion and networking for members to encourage, develop, and advance the skills in the data governance discipline.

  • Big data describes the large volume of data – both structured and unstructured – collected on a day-to-day basis.

  • 3Vs (volume, variety and velocity) are three defining properties or dimensions of big data

Phases of Data processing

Aadhaar and application

National data sharing and accessibility

Open Government Data Initiative

Government Open Data License

Big data processing involves five different phases

  • Data Acquisition and Recording: The first challenge is to set filtering parameters so such that useful data is not discarded.

  • Information Extraction and Cleaning: Information collected from big data sources is not in an analysis ready format. For example, consider the data collected from the school day of a student. An information extraction process is applied on such data to extract the required information from the sources.

  • Data Integration, Aggregation, and Representation: It is not sufficient to merely collect and store data. Suitable database design needs to be carefully executed by trained professionals.

  • Mining: Big data is varied, imprecise, and not structured. Mining such data requires clean and efficiently accessible data.

  • Interpretation: The analysis of big data should enable the correct decisions. Providing such explanation for big data requires efforts

  • The data is released under Government Open Data License- India (GODL-India). This license was published as an “Extraordinary Gazette” on February 2017. The license ensures that the data is not misused or misinterpreted, and that all users have the same and permanent right to use the data. Personal information

    • Data that is non-shareable or sensitive

    • Names, crests, logos, and other official symbols of the data providers

    • Data subject to other intellectual property rights, including patents, trademarks, and official marks

    • Military insignia

    • Identity documents

    • Any data that should not have been publicly disclosed on the grounds provided under section 8 of the Right to Information Act, 2005.

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