DATA ANALYTICS
It is the process of inspecting,cleansing,transforming and modeling data to discover actionable insights that support descision-making.
TYPES OF DATA ANALYSIS
1)Descriptive Analysis,it is based on involving and understanding past data.
2)Diagnostic Analysis,it questions how data is obtained and analyses past data using techniques like drill-down,data discovery and correlations.
3)Predictive Analysis,it makes predictions as to future events by use of historical data.
-it makes use of statistical models and machine learning techniques to forecast future. trends.
4)Prescriptive Analytics,this deals with what is next wwith data recommending actions based on predictive analytics.
-it combines insights from all previous analytics types and uses optimization and simulation algorithims.
DATA ANALYSIS TOOLS.
1) PROGRAMMING LANGUAGES
i)Python,it is a popular close-to-human programming language with libraries such as pandas,Numpy and SciPy which facilitate data analysis tasks.
ii)Sql,it queries and manages dataases.
2)DATA VISUALIZAION TOOLS AND STATISTICAL ANALYSIS TOOLS
i)R,it is a language tailored for statistical analysis and data visualization.
ii)Tableau,it creates interactive and shareable dashboards.
iii)Power BI,it provides interactive visualizations and business intelligence capabilities.
iv)Excel,it is a spreadsheet software that offers basic statistical tools.
v)SAS,it offers GUI and scripting options for advanced analyses and publication of worthy graphics and charts.
3)BIG DATA TOOLS
i)NOSQL Databases(MongoDB),it is designed for storing,retreivng and managing big data.
4)JUPYTER NOTEBOOKS
it provides an interactive environment where users can combine code execution,text and rich media making them an excellent tool for exploratory data analysis and sharing results.
DATA ANALYTICS TECHNIQUES
1)Data Cleaning(Data processing),it involves identifying and correcting errors in the dataset by handling missing values,removing duplicates and correcting incosistencies.
2)Data Exploration and Visualization,it involves examining the dataset structure with visualizaion tools and techniques like histograms,scatterplots and boxplots help in understanding the data underlying patterns and distributions.
3)Statistical Analysis,it forms the backbone of data analysis with techniques like: i)Descriptive statistics,mean,mode,median.
ii)Inferential statistics,hypothesis testing,confidence intervals.
iii)Advanced statistical modelling,regression analysis,ANOVA.
4)Machine Learning,it involves training algorithims to learn from and make predictions on data.common techniques used:
i)Supervised Learning,classification,regression.
ii)unsupervised learning,clustering and associaton.
iii)Reinforcement learning,decision making.
5)Data Mining,it involves discovering patterns in large datasets using methods at the intersecion of machine learning,statistics and database systems.
Techniques used:
i)Association rule learning.
ii)Cluster analysis.
iii)Anomaly detection
6)Time Series Analysis,it focuses on data points collected or recorded at specific time intervals.
Techniques used:
i)ARIMA(Auto-Regressive Integrated Moving Average).
ii)Exponential smoothing.
iii)Seasonal decomposition.
CONCLUSION
Data analytics is an ever-evolving field that leverages various techniques and tools to transform raw data into actionable insights.
whether you're cleaning data, visualizing patterns, or building predictive models, the right combination of methods and technologies can significantly enhance your ability to make data-driven decisions. Embrace these techniques and tools to unlock the full potential of your data and drive impactful outcomes in your domain.
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