Forem: Ann Kigera

The Ultimate Guide to Data Analytics.

Ann Kigera — Wed, 04 Sep 2024 16:41:06 +0000

What is Data Analytics?

The process of collecting, organizing, and transforming data to support conclusions, projections, and well-informed decision-making is known as data analytics. Data Analysis is a subcategory of data analytics that deals specifically with extracting meaning from data.

Data analytics includes data science which refers to using data to theorize and forecast and data engineering which refers to building data systems.

How is Data Analytics used?

It allows companies to roll out targeted content and fine-tune it by analyzing real-time data. Data analytics also provides valuable insights into how marketing campaigns are performing.

Data Analysis Process

identifying the question
collecting raw data
cleaning data
analyzing data
interpreting the results.

Skills required in Data Analytics

Structured query language (SQL) is a programming language for storing and processing information in a relational database. A relational database stores information in tabular form, with rows and columns representing different data attributes and the various relationships between the data values. It is a programming language commonly used for databases

Machine learning (ML) is a branch of Artificial Intelligence and computer science that focuses on the using data and algorithms to enable AI to imitate the way that humans learn.

Statistical programming language typically refers to a programming language that is specifically designed and optimized for performing statistical analysis, data manipulation, and data visualization. R and Python are commonly used to create advanced data analysis programs.

Probability and statistics, in order to better analyze and interpret data trends.

Data visualization, or the ability to use charts and graphs to tell a story with data.

Importance of Data Analytics

Predict future trends: Businesses that employ predictive analysis may develop future-focused products and adapt rapidly to developing market trends, giving them a competitive advantage over their competitors. Depending on the application, the data studied may include historical records or new information processed for real-time analytics.

Measure performance. Data analytics provide organizations with metrics and key performance indicators (KPIs) to track progress, monitor performance and evaluate the success of business initiatives. This helps businesses respond promptly to changing market conditions and other operational challenges.

Improve security. Companies use data analytics methods to look at past security breaches and find the underlying vulnerabilities. Data analytics can also be integrated with monitoring and alerting systems to quickly notify security professionals in the event of a breach attempt.

Personalize customer experiences. By going beyond traditional data methods, data analytics connects insights with actions, enabling businesses to create personalized customer experiences and develop related digital products.

Reduce operational costs. By optimizing processes and resource allocation, data analytics can help reduce unnecessary expenses and identify cost-saving opportunities within the organization.

Provide risk management. Data analytics lets organizations identify and mitigate risks by detecting anomalies, fraud and potential compliance issues.

Understanding Your Data: The Essentials of Exploratory Data Analysis

Ann Kigera — Sun, 18 Aug 2024 15:01:35 +0000

What is Exploratory Data Analysis?

EDA is a tool that is used by Data Scientists which often involves the use of data visualization techniques to analyze, understand and summarize data set's key features.

EDA makes it simpler for data scientists to find patterns, identify anomalies, test hypotheses and identify assumptions to provide answers

EDA offers knowledge of data set variables and the interactions between them. It is mostly used to look into what data can provide beyond the formal modelling. It can also assist in determining the accuracy of the statistical methods you are considering for data analysis.

Importance of EDA in data science

EDA's primary goal is to help in examining data before making any conclusions. It can help in correcting obvious mistakes, better understanding data patterns, spotting patterns or unusual patterns and discovering links between the variables.

Exploratory analysis is a tool that data scientists use to make sure the results they provide are accurate and applicable to any business or company goals. By ensuring stakeholders are posing important questions, EDA also benefits them. Standard variation, Quantitative variables and confidence intervals are among the topics that EDA may assist with. Elements of EDA may be applied to more complex data analysis or modelling such as machine learning.

Tools

python : In order to determine how to handle missing values for machine learning, it is crucial to be able to discover missing values in a data set using Python and EDA combined.
R : When making statistical observations and performing data analysis, statisticians in the field of data science frequently utilize the R language.

Types of Exploratory Data Analysis

univariate non geographical
This is simplest form of data analysis, where the data being analyzed consists of just one variable. Since it’s a single variable, it doesn’t deal with causes or relationships. The main purpose of univariate analysis is to describe the data and find patterns that exist within it.
univariate geographical
Non-graphical methods don’t provide a full picture of the data. Graphical methods are therefore required.
Multivariate nongraphical
Multivariate data arises from more than one variable. Multivariate non-graphical EDA techniques generally show the relationship between two or more variables of the data through cross-tabulation or statistics.
Multivariate graphical
Multivariate data uses graphics to display relationships between two or more sets of data. The most used graphic is a grouped bar plot or bar chart with each group representing one level of one of the variables and each bar within a group representing the levels of the other variable.

The Ultimate Guide to Data Analytics: Techniques and Tools

Ann Kigera — Sun, 04 Aug 2024 13:50:21 +0000

Introduction to Data Analytics

The technique of examining data in order to gather valuable insights that can be used to guide insightful business decisions is known as data analytics. Data analytics is used to solve challenges within an organization. Patterns in a dataset will show relevant information in a specific area for example the temprature at a particular time.

Data analytics uses past data to predict the future behaviors therefore making informed decision on the information from the data.

Data Analyst

The work of a data analyst is to gather and combine information from variety of sources, to ensure accuracy and dependability, to clean up and preprocess data, to analyze exploratory data to find trends, patterns and irregularities.

A data analyst extracts raw data, organizes and analyzes the data. After interpreting the data, the data analyst will transfer their findings on what the next step should be.

Data Analysis Process

identify the data required
Collection of data
Data cleaning
Data Analysis
Data Interpretation and visualization

Data Analysis Techniques

Data Analysis techniques and methods fall under two main types namely:

Qualitative Data Analysis - this method extracts data from texts or words, pictures, symbols and observations
Quantitative Data Analysis - this method turns raw datasets into numerical data.

Top Techniques for Analyzing Data

Neural Network
Cohort Analysis
Time Series Analysis
Factor Analysis
Regression Analysis
Cluster Analysis
Data Mining
Conjoint Analysis
Multidimensional Scaling
Decision Trees
Context Analysis
Text Analysis

Tools

Data Analysts use tools such as Microsoft Excel, Power BI, Tableau, Jupyter Notebook, Statistical Analysis System(SAS) and programming languages such as SQL, R and Python. Such tools help data analysts to carry out various tasks such as data mining, statistical analysis, database management and reporting.

Skills

The hand on skills required for one to become a data analyst are statistics, knowledge of programming languages such as SQL, R and Python and data visualization.