10 Essential Key Skills of Data Analytics

 10 Essential Key Skills of Data Analytics

Introduction

This article/blog is specifically designed to discuss the use and importance of Data analytics in day-to-day business.

Data Ecosystem

·       A data ecosystem refers to a combination of enterprise infrastructure and applications that are utilized to aggregate and analyze information.

·       Data ecosystem is a complex environment of co-dependent networks and actors that contribute to data collection, transfer, and use.

·       A data ecosystem collectively refers to all the programming languages, algorithms, applications, and the general infrastructure used to collect, analyze, and store data.

·       Data ecosystem enables organizations to better understand their customers and craft superior marketing, pricing, and operations strategies.

·       For example, a client’s medical data is shared with an insurance company to calculate a premium.

·       Using modern technologies like AI and cloud computing, businesses can leverage their data ecosystem to gain significant value from their data assets.

Data

Data refer to the collection of facts and instructions that are used in a formalized manner so that they can be used as valuable information.

Data dashboards

·       Data dashboards are a summary of different, but related datasets, presented in a way that makes the related information easier to understand.

·       Dashboards are a type of data visualization and often use common visualization tools such as graphs, charts, and tables.

Structured data

·       Structured data refers to organized data in a fixed field within a file or record.

·       Structured data is highly organized and easily understood by machine learning.

·       Structured data is typically stored in RDBMS.

·       Some common examples of structured data are names, dates, credit card numbers and employee numbers, and stock information.

Unstructured data

·       Unstructured data refers to information that is either not organized or does not have a pre-defined data model.

·       Some common examples of unstructured data are Rich media, surveillance data, media and entertainment data, geospatial data, audio, and weather data.

Big data

·      Big data refers to extremely large data sets that can be analyzed computationally to reveal patterns, trends, and associations, particularly in relation to human interaction and behavior. 

·       Big data sets are voluminous that traditional data processing software cannot manage them.

Labels

·       Labels are the final output in ML.

·       A label is added to data to make it more meaningful and informative to provide context so that a machine learning model can learn from it.

·       Labels make that data specifically useful in certain types of ML known as supervised ML setups.

·       Some common examples of labels in ML are the future price of wheat, the kind of animal shown in a picture, and the meaning of an audio chip.

Labeled data

·       Labelled data refers to a designation for pieces of data that have been tagged with one or more labels identifying certain properties or characteristics or classifications or contained objects.

·       Data labeling is the process of identifying raw data (images, text files, and videos.)

·       Labelled data comes with a tag, like a name, a type, or a number.

·       Some common examples of labeled image datasets are cat photos or dog photos.

Unlabelled data

·       Unlabelled data refers to a designation for pieces of data that have not been tagged with labels identifying characteristics, properties or classifications.

·       Unlabelled data is a type of data that comes with no tag.

Text mining

·       Text mining is also known as text data mining or text analysis or textual analysis.

·       Text mining refers to the process of deriving high-quality information from text.

·       Text mining is the process of transforming unstructured text into a structured format to identify meaningful patterns and new insights.

·       Text mining is the automated process of classifying and extracting text data using AI.

·       A text mining model can read and understand text in an Excel spreadsheet and structure it automatically.

·       Some common examples of text mining data are call-center transcripts, online reviews, and customer surveys.

Analytics

·       Analytics refers to a field of computer science that uses math, statistics, and Machine Learning (ML) to find meaningful patterns in data.

·       Analytics is the process of discovering, interpreting, and communicating significant patterns in data.

·       Analytics helps us see insights and meaningful data that we might not otherwise detect.

Data Analytics

·       Data analysis refers to the process of inspecting, cleansing, transforming, and modeling data to discover useful information, inform conclusions, and support decision-making.

·       Data analytics refers to the science of analyzing raw data to draw conclusions about that information and find insights, meaningful patterns, and trends.

·       Data analytics is widely used for the discovery, interpretation, and communication of meaningful patterns in data within the organization.

·       Data analytics help companies better understand their customers, evaluate their ad campaigns, personalize content, create content strategies, and develop products.

·       Data analytics is used to boost performance and improve the bottom line.

·       Data analytics helps businesses get real-time insights about sales, marketing, finance, and product development.

Google Data analytics

Google Data Analytics is a web analytics service that provides statistics and essential analytical tools for SEO and marketing.

Google Analytics

·       Google Analytics is a platform that comes under the Google Marketing platform brand.

·       Google Analytics refers to a web analytics service offered by Google that tracks website performance, collects visitor insights, and reports website traffic.

·       Google Analytics is used to track website activity such as session duration, pages per session, and bounce rate of individuals using the site along with the information on the source of the traffic.

·       Google Analytics provides access to a massive amount of data related to how users find and interact with the sites. For example, you can see how many people visited a specific page, how long they remained there, where your users live, and how certain keywords perform.

·       Google Analytics helps to identify which social media platforms drive the maximum, targeted traffic to your site.

·       Google Analytics helps you to measure your advertising ROI as well as track your Flash, video, and social networking sites and applications.

·       Google Analytics provides you with tools to better understand your customers.

·       Google Analytics is a powerful tool for brands, bloggers, and businesses.

Some typical sources of data for Data Analytics

·       Excel spreadsheet

·       Online sources

·       Collection of cloud-based data

· On-premises hybrid data warehouses

Some common areas where data analytics is widely used

·       Serving customers with useful products

·       Driving marketing campaigns for businesses

·       Improving the insurance industry

·       Creating manufacture warranties that make sense

·       Promoting smart energy usage for utility companies

·       Stopping hackers in their tracks (used for security purposes)

·       Revealing trends for research institutions (used for education purposes)

·       Increasing the quality of medical care

·       Fighting climate change in local communities

·       Data analytics plays a vital role in analyzing surveys, polls, and public opinion. Data analytics help segment audiences by different demographic groups and analyze attitudes, patterns, and trends in each of them, producing a more situation-specific, accurate, and actionable form of public opinion.

Some common key skills of Google Data analytics

Data collection

·       Data collection is also called data gathering.

·       Data collection is the process of collecting and analyzing information on relevant or targeted variables in a predetermined or established methodical way so that one can respond to some specific research questions, test hypotheses, and assess results.

·       Data collection is generally done after the experiment or observation.

·       Data collection is either qualitative or quantitative.

·       Data collection can help improve services, understand consumer needs, grow and retain customers, refine business strategies, and even sell the data as second-party data to other businesses at a profit.

Data cleaning

·       Data cleaning is also known as data cleansing or data scrubbing.

·       Data cleaning refers to fixing incorrect, incomplete, duplicate, or otherwise erroneous data in a data set.

·       Data cleaning involves identifying data errors and then changing, updating, or removing data to correct them.

Data visualization

·       Data visualization means representing information and data using charts, graphs, maps, and other visual tools.

·       Data visualization helps tell a story with data, by turning spreadsheets of numbers into stunning graphs and charts.

·       Data visualization allows us to easily understand any patterns, trends, or outliers in a data set.

·       Data visualization presents data to the general public or specific audiences without technical knowledge in an accessible manner.

·       Data visualization helps us to drive informed decision-making.

·       Data visualization is a powerful way for people, especially professionals, to display data so that it can be interpreted easily.

·       Some common benefits of data visualization are story-telling, accessibility, visual relationship, and exploration.

Data analytics tools

·       Data analytics tools refer to software that collects and analyze data about a business, its customers, and its competition to improve processes and help uncover insights to make data-driven decisions.

·       Data analytics tools enable businesses to analyze vast data collections for great competitive advantage.

Metadata

·       Metadata is data that describes other data.

·       Metadata describes whatever piece of data it’s connected to whether that data is video, a photograph, web pages, content, or spreadsheets.

·       Metadata is information about your content that search engines use to index your page.

·       The Metadata API returns the list and attributes of columns (i.e. dimensions and metrics) exposed in the Google Analytics reporting APIs. Attributes returned include UI name, description, segments, and support.

·       The Metadata API can be used to automatically discover new columns.

Spreadsheet

·       Spreadsheets are computer programs that display information in a two-dimensional grid and formulas.

·       A spreadsheet is designed to hold numerical data and short text strings.

·       A spreadsheet can capture, display, and manipulate data arranged in rows and columns.

·       Some common examples of spreadsheet programs are Google Sheets (online and free), LibreOffice-calc (free), Microsoft Excel, and OpenOffice- calc (free).

R-programming

·       R-programming refers to a programming language and free open-source software environment for statistical computing and graphics.

·       R is one of the most comprehensive statistical programming languages available, capable of handling everything from data manipulation and visualization to statistical analysis.

·       R is widely used in data science by statisticians and data miners for data analysis and the development of statistical software. 

·       R-programming is used for various data science, statistics, and visualization projects.

R Markdown

·       R Markdown is text-based formatting that allows you to embed code and explanatory text in the same document.

·       R Markdown is a specific type of file format designed to produce documents that include both code and text.

·       R Markdown files (.Rmd) can be rendered to other formats (e.g. HTML, pdf, docx) to generate reports or web applications.

·       R Markdown is a very simple markup language that provides methods for creating documents with headers, images, code chunks, and links from plain text files.

·       The three basic components of an R Markdown document are the metadata, text, and code.

·       R Markdown combines text and code pieces in one document, and can easily combine introductions, hypotheses, the code that is running, the results of that code, and the conclusions all in one document.

R Studio

·       R Studio is an IDE for R and Python.

·       R Studio is a programming language for statistical computing and graphics.

·       R Studio includes a console, and syntax highlighting editor that supports direct code execution, as well as tools for plotting, history, debugging, and workspace management.

·       R Studio is used in data analysis to import, access, transform, explore, plot, and model data, and for machine learning to make predictions on data.

·       R Studio software is ideal for data scientists, Dev Ops engineers, and IT admins.

·       R Studio is incredibly user-friendly.

·       R Studio is available in two formats: a) R Studio desktop is a regular desktop application. b) The R Studio server runs on a remote server and allows accessing R Studio using a web browser.

SQL databases

·       SQL database refers to a collection of tables that stores a specific set of structured data.

·       SQL database is required to handle structured data which is stored in relational databases.

·       SQL databases are essential for carrying out data wrangling and preparation.

·       RDBMS like MS SQL server, IBM, DBL, Oracle, My SQL, and MS Access can be used for data science. 

Data aggregation

·       Data aggregation is collecting data to present it in summary form.

·       Data aggregation is the process of summarizing a large pool of data for high-level analysis.

·       Data aggregation may be performed manually or through specialized software.

·       Data aggregation is used to get summarized data for analytics.

·       Data aggregation provides statistical analysis for different objectives.

·       Data aggregation involves compiling information from a range of prescribed databases and organizing it into a simpler, easy-to-use medium, usually utilizing sum, average, mean, or median references.

Data ethics

·       Data ethics is a branch of ethics that evaluates data practices like collecting, generating, analyzing, and disseminating data, both structured and unstructured–that have the potential to adversely impact people and society.

·       Data ethics refers to the principles behind how organizations gather, protect, and use data.

·       The five Cs of data ethics are consent, clarity, consistency, control (and transparency), and consequences (and harm).

·       Five principles of data ethics for business professionals are ownership, transparency, privacy, intention, and outcomes.

Tableau

·       Tableau is a software company offering collaborative data visualization software for organizations with business information analytics.

·       Tableau is used to visualize data and reveal patterns for analysis in business intelligence, making the data more understandable.

·       Tableau is a leading data visualization tool used for data analysis and business intelligence.

Data integrity

·       Data integrity is a concept and process that ensures the accuracy, completeness, consistency, and validity of an organization’s data.

·       Data integrity can be measured by the number of errors, duplicates, or missing values in your data.

·       A good example of data integrity is that customer data stored in a cloud database is accurate and free from corrupting influences such as malware or unauthorized access.

Questioning

·       Questioning is the process of forming and wielding that serves to develop answers and insight.

·       Questioning is a major form of human thought and interpersonal communication.

Decision making

Decision-making is the process of making choices by identifying a decision, gathering information, and assessing alternative resolutions.

Data-driven Decision making

Data-driven decision-making is the process of collecting data based on your company’s key performance indicators (KPIs) and transforming that data into actionable insights.

Sample size determination

Sample size determination is the act of choosing the right number of observations or people from a larger group and replicates to include in a statistical sample.

Exploratory Data Analysis (EDA)

·       EDA is one of the basic and essential steps of a data science project.

·       EDA is an approach that is used to analyze the data and discover trends, and patterns or check assumptions in data with the help of statistical summaries and graphical representations.

·       A data scientist involves almost 70% of his work in doing the EDA of his dataset.

·       EDA is used by data scientists to analyze and investigate data sets and summarize their main characteristics, often employing data visualization methods.

Visualization using Python

Python provides various libraries that come with different features for visualizing data. All these libraries come with different features and can support various types of graphs. Four libraries are Matplotlib, Seaborn, Bokeh, and Plotly.

Matplotlib

·       Matplotlib is an easy-to-use, low-level data visualization library that is built on NumPy arrays.

·       Matplotlib consists of various plots like scatter plots, line plots, and histograms.

·         Matplotlib provides a lot of flexibility.

Descriptive statistics

·       Descriptive statistics refers to a set of methods used to summarize and describe the main features of a dataset, such as its central tendency, variability, and distribution.

·       Descriptive statistical methods provide an overview of the data and help identify patterns and relationships.

·       Descriptive statistics has two main types- a) Measures of central tendency (Mean, Median, Mode) and b) Measures of Dispersion or variation (variance, standard deviation, range).