Data Analysis
Data Analysis:
Data analysis plays a very vital role in getting the right result of your analysis by facts.
Data analysis plays a vital role in reaching towards right results. Data analysis is a body of methods that help to describe facts, detect patterns, develop explanations, and test hypotheses. It is used in all of the sciences. It is used in business, in administration, and in policy
Few of the Questionnaire that need to be known for Data Analysis are as follows :
Is there sufficient data to know whether your hypothesis is correct? Is your data accurate? Have you summarized your data with an average, if appropriate? Does your chart specify units of measurement for all data? Have you verified that all calculations (if any) are correct?
Course Content for DATA Analysis :
Module 1: Introduction to Data Analysis
- What is Data Analysis?
- Importance of data in decision-making
- Types of data (structured, semi-structured, unstructured)
- Data analysis life cycle
- Role of a Data Analyst
Module 2: Basics of Statistics & Probability
- Descriptive statistics (mean, median, mode, variance, standard deviation)
- Probability concepts
- Hypothesis testing
- Correlation & regression
- Sampling techniques
Module 3: Data Handling with Excel
- Data cleaning and preparation in Excel
- Pivot tables, charts, dashboards
- Excel formulas for data analysis
- Case studies using Excel
Module 4: SQL for Data Analysis
- Introduction to databases
- SQL basics: SELECT, WHERE, GROUP BY, ORDER BY
- Joins, Subqueries, Window functions
- Aggregations and reporting with SQL
- Hands-on projects with real datasets
Module 5: Data Analysis with Python / R
- Introduction to Python/R for data analysis
- Libraries: Pandas, NumPy, Matplotlib, Seaborn (Python) | dplyr, ggplot2 (R)
- Data wrangling and cleaning
- Exploratory Data Analysis (EDA)
- Visualization techniques
Module 6: Data Visualization & Reporting
- Principles of effective data visualization
- Tools Learning
- Building interactive dashboards
- Storytelling with data
- Business reporting
Module 7: Advanced Analytics
- Predictive modeling basics
- Introduction to machine learning for data analysts
- Regression, classification, clustering (overview)
- Time series analysis, Case studies
Module 8: Business Applications of Data Analysis
- Marketing & customer analytics
- Financial data analysis
- Operations and supply chain analytics
- HR analytics
- Case study discussions
Module 9: Data Ethics & Governance
- Data privacy & security
- Ethical use of data
- Data governance & compliance (GDPR, HIPAA, etc.)
Module 10: Capstone Project
- End-to-end data analysis project
- Real-world dataset (finance, retail, healthcare, etc.)
- Data cleaning → Analysis → Visualization → Reporting → Insights
Expertise of Mentor / Trainers :
- Trainers has sound expertise over working as a mentor / statistician or project implementer at multiple organizations where they have extensive experience in the development of statistical models, variation based analysis, designed experimentation, and statistical process control.
- Currently team consist of Consultants/Sr. Consultants/Principal Consultants where they assists clients, manufacturers in the application of statistical methods to reduce variation and improve quality and productivity.
- Also, possesses expertise in the formation of prediction models & application of various methods to achieve robust and reliable products as well as to estimate and reduce variation.
- In addition to providing consulting services, they regularly conducts workshops in industrial statistical methods for companies.
