Data Analysis Course
July 21, 2023 2023-07-21 16:59Data Analysis Course
Data Analysis Course
Course Overview
- Enhanced Problem-Solving : By giving you the tools to recognize trends, spot anomalies, and solve complicated issues with data-driven methods, data analytics enables you to take well-informed decisions.
- Improved Forecasting and Strategic Planning : Data analytics enables accurate forecasts and successful business strategies for improved forecasting and planning by analyzing patterns and trends from previous data.
- Practical Application in Everyday Life : Data analytics expertise has real-world applications in daily life. Effective data organization may help with personal budgeting, and clearly communicating complicated data enhances communication abilities.
- Empowered Decision-Making : Your ability to extract important insights from complicated datasets thanks to your proficiency in data analytics will help you to make well-informed judgments in a variety of industries, including business, finance, and healthcare.
Skills You Will Gain
The students will acquire following set of skills after completing this Training Course.
- Data Visualization : Master the skill of using interactive visualizations, charts, and graphs to display complicated data in a way that is visually appealing and ensures stakeholders can understand and digest the information with ease.
- Data Mining : Learn how to use methods like clustering, classification, and association analysis to extract hidden patterns, trends, and insights from huge datasets, allowing decision-making based on insightful data findings.
- SQL and Database Querying : Gain proficiency in relational database extraction, manipulation, and analysis using SQL (Structured Query Language), improving data analysis procedures and boosting data-driven insights.
- Programming Proficiency : Become proficient in programming languages like Python to automate activities, analyze data more effectively, streamline workflows, and produce more significant outcomes.
- Business Acumen : Develop a thorough grasp of business principles and subject-matter expertise, coordinating data analysis activities with corporate goals, and making data-driven suggestions that promote company success.
Career Path
- Data Analyst
- Business Intelligence Analyst
- Powe BI Analyst
- Data Engineer
- Market Research Analyst
- Financial Analyst
- Data Quality Analyst
- Healthcare Analyst
Projects
- Weather Prediction
- Predicting Cancer Malignant or Benign
- Predicting Stock Prices
- Wine Quality Prediction
Certification
Participants will receive certificates from Future Connect Training to mark their success and the priceless information they have learned after completing the training program.
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Power BI
Power BI is a powerful data visualization tool that enables analysts to create interactive dashboards, reports, and visualizations. With its user-friendly interface and robust features, Power BI empowers users to transform raw data into insightful and visually engaging representations. Analysts can easily explore data, identify trends, and communicate findings effectively, making Power BI an indispensable tool for data-driven decision-making.
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Excel (Basic, Intermediate, Advanced)
Excel skills enable users to manage, modify, and analyze massive datasets with ease. It includes vital components like PivotTables and PivotCharts as well as sophisticated operations like VLOOKUP, INDEX, MATCH, and IF expressions. Advanced Excel is well known and frequently used in data analysis because of its adaptability, flexibility, and user-friendly interface.
- 1. The basic IT functions
- 2. Creating and using a range of name
- 3. Locate formulas
- 4. IFERROR
- 5. VLOOKUP & HLOOKUP
- 6. INDEX & MATCH
- 7. Analyzing Your Data
- 7.1. Set up Pivot tables
- 7.2. Grouping your data
- 7.3. Amend Pivot Tables with new data
- 7.4. Use a slicer to filter your data
- 7.5. Combining slicers to more than one Pivot Table
- 7.6. Using a timeline
- 7.7. Organize a Pivot chart
- 8. Reducing Your Audit Risk
- 8.1. Data Recognition
- 8.2. Use of Trace Precedents
- 8.3. Use of Trace Dependents
- 8.4. Eliminate Arrows
- 8.5. Flaws checking
- 8.6. Check out Formula
- 8.7. Watch Window
- 9. Enhance Your Workflow
- 9.1. Set up Macro security
- 9.2. Recording Macros
- 9.3. How to edit Macro
- 9.4. Understanding the VBA edit window
- 9.5. Allow & run a Macro from the ribbon
- 9.6. Saving & using a Macro-enabled Workbook
- 9.7. Deleting your Macro
- 10. Additional Topics
- 10.1. Scenarios
- 10.2. Goal Seek
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Mathematics & Statistics
Mathematics and statistics are crucial in machine learning, providing tools for creating models that learn from data and make accurate predictions. Linear algebra, calculus, and probability theory are used in algorithms, while statistics analyzes data, identifies patterns, and predicts outcomes. Strong knowledge in these areas is necessary for success in machine learning.
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SQL(Structured Query Language)
SQL is critical in machine learning, allowing efficient data retrieval and analysis from relational databases. It ensures accurate analysis of large data sets, with solid understanding essential for success.
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Python
Python is a widely-used programming language in machine learning. It offers powerful libraries, tools, and frameworks for data analysis, modeling, and visualization. Python's simplicity and flexibility make it a popular choice for building machine learning models and deploying them in real-world applications.
- 1. Python Setup and What is Python?
- 2. Data Types and Syntax
- 3. Comparison Operators
- 4. Python Loop
- 5. Python Statements
- 6. Logical Operators
- 7. Methods and Functions
- 8. Error and Exception Handling
- 9. Modules Packages and libraries
- 10. Debugging
- 11. Advanced python Modules (DateTime)
- 12. File Management
- 13. Multiple Activities to Perform
- 14. Multiple Projects to Build
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Machine Learning
Machine learning is a subset of artificial intelligence that enables computers to learn from data and make predictions or decisions without being explicitly programmed. It involves the use of algorithms, statistical models, and data to build predictive models and uncover insights from large datasets.
- 1. Data Preprocessing
- 2. Supervised Learning
- 2.1. Regression Models
- 2.1.1. Simple Linear Regression
- 2.1.1. Multiple Linear Regression
- 2.1.2. Polynomial Regression
- 2.1.2. Random Forest Regression
- 2.1.3. Topics such as
- 2.1.3.1. Assessing a Regression Model
- 2.1.3.2. Bias vs Variance
- 2.1.3.3. Regularisation
- 2.1.3.4. Gradient Descent
- 2.2. Classification Models
- 2.2.1. Decision Tree Classification
- 2.2.2. K-Nearest Neighbor
- 2.2.3. Logistic Regression
- 2.2.4. Naïve Bayes
- 2.2.5. Random Forest Classification
- 2.2.6. Support Vector Machines
- 2.2.7. Additional Topics
- 2.2.7.1. Assessing a Classification Model
- 2.2.7.2. Adaboost
- 2.2.7.3. Gradient Boosting
- 2.2.7.4. XGBoost
- 2.2.7.5. Grid Search CV
- 3. Unsupervised Learning
- 3.1. Clustering Models
- 3.1.1. Hierarchical
- 3.1.2. K-Means Clustering
- 3.2. Association
- 3.2.1. Apriori
- 3.2.2. Eclat
- 4. Build Dashboards for Data Visualisation
- 5. Solved Sample Code Files for easy practice
- 6. Access to Multiple Datasets
- 7. 7+ Real world data projects
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ETL/ELT
ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are data integration processes used to extract data from various sources, transform it into a desired format, and load it into a target data warehouse or data lake for analysis and reporting. Both ETL and ELT play a crucial role in consolidating, cleaning, and preparing data for business intelligence and analytics purposes.
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Git And GitHub
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Python GUI And SQLite (Add On)