Data, Statistics, and Statistical Analysis Certification Training Course

Why Select this Training Course?

Data is collected and analyzed statistically to reveal hidden trends and patterns in the business. It’s an essential part of any data analysis. To successfully lead their firms, business managers may use the results of statistical studies to assess previous performance and foresee possible future business practices. Statistics may be used to characterize markets, guide advertising and pricing, and adapt to changes in customer demand.

What is the data analysis process?

Data analysis is a process that involves collecting, cleaning, analyzing, transforming, and modeling data sets to gain valuable insights into a phenomenon of interest. The insights are used to reach informed decisions that help businesses and companies become more profitable.

The data analysis process starts with collecting data from different sources, and the data gathered are cleaned and prepared. The second step consists of analyzing data; under this step, data collected are evaluated, and continuous running improves a model. The final stage in the analysis process is the generation of reports.

What are the seven types of statistical analysis?

Statistics is an aspect of science that utilizes different analytical techniques and tools to manage data. Most organizations depend on statistical analysis to organize data and predict future trends. The seven data analysis types include descriptive, inferential, prescriptive, causal, predictive, mechanistic, and exploratory data analysis.

The Rcademy Data, Statistics, and Statistical Analysis Certification Training Course is curated to help participants understand, model, and transform data to provide valuable information that supports decision-making. The course covers essential analytical tools and concepts in managing, classifying, and interpreting data sets to discover correlations and hidden patterns.

Participants will also learn how to design reproducible data analysis reports, execute Bayesian and frequentist statistical inferences, and communicate statistical results properly. Focus is also given to data visualization and forecasting, risk simulation, predictive analysis, and network analysis. The course contents are exhaustive, and attendees will also learn data merging and variance, data creation and modification, flow control in statistics, exponential distribution, probability, and probability distribution, and the Bayes theorem.

Who Should Attend?

The Data, Statistics, and Statistical Analysis Certification Training Course by Rcademy is suitable for several people, particularly those invested in data and statistical management. The following should undertake the course:

• Data analysts: responsible for the collection and interpretation of data
• Business analysts: charged with conducting market analysis, evaluation of product lines, and a business’ overall profitability
• Statisticians: tasked with collecting numerical data and interpreting them to help companies identify trends and make predictions
• Finance analysts: charged with tracking a firm’s financial transactions, evaluating market conditions and business performances to generate forecasts
• IT professionals: responsible for upgrading and installing components, setting up software, and helping with network administration
• Data technicians: tasked with managing a company’s data management systems and monitoring the security and safety of the information stored
• Marketing analysts: charged with supervising market conditions to assist companies in identifying products in demand and customers that will purchase them
• Operations analysts: responsible for evaluating a company’s policies, functions, and procedures to discover possible areas for improvements
• Healthcare analysts: tasked with analyzing medical data to enhance the business department of hospitals and medical institutions
• Chief Accounting Officers: responsible for overseeing the accounting functions of a company and also ensuring the company is tax compliant
• Biostatisticians: tasked with developing biological experiments within agricultural and medical industries
• Professionals interested in understanding the rudiments of data and statistical analysis

What are the Course Objectives?

The Data, Statistics, and Statistical Analysis Certification Training Course by Rcademy aim to help participants achieve the following:

• Understand the rudiments of statistical analysis and how businesses utilize it in improving revenue generation
• Execute hypothesis tests, interpret statistical data, and report results of analyses to clients and customers
• Evaluate and use regression models in examining the connections between multiple variables
• RRecognizethe various types of data, structure, and how to utilize data in solving real-life problems
• Understand the rudiments of data storytelling with visualizations
• Understand how to use interactive graphical techniques in visualizing and analyzing data sets
• Design predictive models using a variety of machine learning and statistical algorithms, and assess their performance
• Understand how to use linear programming and related techniques to solve constrained optimization challenges

How will this Course be Presented?

The contents of this course are curated solely to satisfy and boost participants’ skills in data and statistical analysis; it is participant-based. Different quality methodologies and techniques are employed to ensure constant participation and satisfaction of participants. The course will be taught by seasoned professionals in the field and extensively researched modules that thoroughly deal with data and statistical analysis principles and practice.

The Rcademy course on Data, Statistics, and Statistical Analysis Certification Training comprises theory and practical learning delivered through lecture materials, case studies, visual aids, group discussions, problem-solving notes, evaluation of class activities, seminars, quizzes, and individual reports after each interaction.

What are the Topics Covered in this Course?

Module 1: Introduction to Data Analysis

• Basics of data analysis
• Sampling and exploring data
• Fundamentals of Bayes’ rule
• Types of sampling methods
• Impacts of data sampling on Inference
• Basic data visualization
• Summary statistics
• Data analysis with R
• Installing and utilizing RStudio (free statistical software) and R

Module 2: Introduction to Statistical Analysis

• Introduction to statistical analysis
• Types of statistical analysis
  – Descriptive
  – Causal
  – Predictive
  – Inferential
  – Mechanistic
  – Exploratory
  – Prescriptive
• Central tendencies
• Statistical and data analysis tools
• Moving averages with data analysis
• Dispersion using data analysis
• Correlation and regression

Module 3: Essentials of Statistics and Machine Learning

• Introduction to machine learning with Python
• Big Data in machine learning
• Data mining
• Basics of statistical sampling
• Sampling methods in machine learning
• Analytics in machine learning
• Emerging trends in machine learning
• Technical terminologies
• Basics of data types and visualization

Module 4: Basics of Probability in Data Analysis

• Fundamentals of probability
• Correlation vs causation
• Basics of statistical probability
• Conditional probability
• The concept of independence
• Relative frequency of probability
• Probability distribution
• Total Probability
• Random variables

Module 5: Inferential Statistics

• Setting up and performing hypotheses tests
• Reporting results
• Interpreting p-values
• Matrix algebra using mathematical computations and expressions
• Properties of matrix
• Determinants
• Transpose of matrix

Module 6: Statistics for Data Science through Python

• Introduction to data science
• Basics of reservation techniques
• Summation of elements
• Output of variables
• Ordinary least square regression techniques
• Correlation functions
• Exclusive events
• Methods of testing
• Analyzing test statistics

Module 7: Fundamentals of Statistics for Analytics

• Introduction to the components of statistics
• Variable and types of variables
• Understanding ordinary scales
• Bar chart using R
• Quantitative and qualitative research
• Graphical techniques
• Scatter plots
• Entering the value of two variable
• Basics of Excel and data analysis
• Sampling techniques
• Histogram bar charts

Module 8: Forecasting Analytics

• Naïve forecasts
• Time series components
• Performance evaluation
• Forecasting vs evaluation
• Visualizing time series
• Differencing
• Model-driven vs data-driven methods
• De-trending and seasonal adjustments
• Centered and trailing Moving Average (MA)
• Exponential smoothing

Module 9: Averages and How to Calculate Them

• The various types of measures of centrality
• The mode
• The median
• The arithmetic mean
• Visualizing the shape of data
• Variability in data and how to quantify it
  – ­Standard error
  – Standard deviation
• Different techniques of data dispersion

Module 10: Visualising Relationships in Data

• Estimation
• Maximum likelihood
• Outliers and normal distribution
• Inference
• Linear regression and correlation
• Distribution manipulation
• Binomial distribution
• Manipulating distribution
• Variance and deviation
• Importance of data distribution