Introduction
The Advanced Applied Data Analysis training course provides you with the skills to become an effective Data Analyst capable of making sound investment & financial decisions for your personal and organizational benefit. The training course will improve your ability to plan, monitor and control the performance of your organization and investments. This will strengthen your ability within the workplace, allowing you to understand, produce and evaluate data to support you in your role for the benefit of yourself and your organization.
Delegates will work through practical case studies. With the aid of clear explanations, discussions, and detailed material in both hard and soft copy, it will enable you to transfer advanced skills and knowledge to your workplace.
Goals
Participants will learn:
- Advanced Analytical skills to improve your understanding and decision making.
- Communication skills supported by graphical and statistical analysis.
- Advanced Excel skills to aid presentation and analysis of business data.
- An ability to forecast data.
- Advanced Statistical knowledge and skills.
- An ability to apply Data Analysis Techniques to the workplace.
TRAINING OBJECTIVES
- Collect, Present, Analyze and Evaluate Data.
- Develop and use Pivot Tables.
- Analyze data using Data Analysis Tools in Excel.
- Perform Linear Regression; Non-linear regression and Multiple Regression using Excel.
- Apply Data Analysis Techniques to improve Project Management.
- Apply Data Analysis Techniques to improve Financial Management.
Training Methodology
This training course makes extensive use of Excel and the Data Analysis Tools to provide you with Advanced Data Analysis skills. This combined with presentations, explanations, interactive practical exercises, supported by video materials will ensure delegates leave the program as Advanced analysts.
TRAINING SUMMARY
This training covers the Advanced Applied Data Analysis Techniques in order to make meaningful business decisions and improve efficiency & profits. This will strengthen your ability within the workplace, allowing you to make a significant impact as an advanced analyst, providing, using or evaluating data to improve performance, efficiency and profit.
Target Groups
- Professionals who wish to gain an Advanced knowledge of Data Analysis in order to improve their analytical skills and understanding of data.
- Personnel in roles where they need to produce Advanced data analysis and / or use data to make decisions.
Program Content
Day 1: Data Collection, Presentation and Analysis
- Data and Methods of Collecting Data.
- Presentation of Data using Excel.
- Statistical Analysis of Data – Mean, Median and Mode; Variance and Standard Deviation.
- Probability – Binomial, Normal and Poisson Distribution.
- The Limitations of Data and Data Analysis.
- Confidence Intervals and Hypothesis Testing.
Day 2: Pivot Tables and Data Analysis Tools in Excel
- Pivot Tables – developing and using Pivot Tables to aid Decision Making.
- Cross Tabulation and Chi-squared.
- Data Analysis Tools in Excel – a detailed understanding.
- ANNOVA, t-tests and Z scores.
- Moving Average and Exponential Smoothing.
- Advanced and Applied Solver and Goal Seek.
Day 3: Advanced Correlation and Regression
- Co-variance and Correlation.
- Linear Regression.
- Non-linear Regression.
- Multiple Regression using Excel.
- Statistically Testing Results.
- Making Decisions – Project Planning, Estimating Costs; Reducing Risk.
Day 4: Project Management and Applied Data Analysis
- Forecasting Costs and Production levels.
- Estimating Project Duration using Statistical estimates and P.E.R.T.
- Network and Critical Path Analysis.
- Economic Quantity Order Analysis – to optimize inventory levels.
- Linear Programming and Optimization Techniques to optimize resource allocation e.g. inventory and capital.
- Earned Value Analysis – to identify Project Cost and Schedule Variances.
Day 5: Financial Management and Applied Data Analysis
- Analyzing and Forecasting Volatility in the Market, e.g. Oil Prices.
- Evaluating Suppliers, Competitors and Acquisition Targets using Data Analysis.
- Developing Financial Models.
- Cost, Volume, Profit Analysis.
- Risk Management using Data Analysis.
- Evaluating the impact of decisions on Return on Investment and Return on Equity.
