Master business statistics for data-driven decisions and elevate your analytical skills. This course empowers you to confidently analyze business cases and derive actionable insights using advanced statistical tools.

Business Analytics Course: Statistical Insights for Success

# Unlock Your Potential with Advanced Business Analytics!

Embark on a transformative journey to master the art of business analytics through advanced statistical methods. This course empowers you to elevate your analytical skills, enabling you to make data-driven decisions that propel your organization to new heights. Prepare to dive deep into statistical tools, uncover key performance indicators, and develop actionable strategies that drive success in real-world business scenarios. Your journey will culminate in a practical project where theory meets practice, equipping you with the confidence to tackle complex business challenges head-on.


### Who is it for?

This course is designed for aspiring business analysts, data enthusiasts, and decision-makers who are eager to enhance their analytical skills.

- Aspiring business analysts looking to enhance their skills
- Data enthusiasts wanting to leverage statistics in decision-making
- Business managers aiming to make data-driven decisions

**Recommended skill level: Intermediate**


### Prerequisites

To ensure you get the most out of this course, a basic understanding of statistics, familiarity with business analysis concepts, and experience with Excel or statistical software are essential.

- 📊 Basic understanding of statistics
- 📈 Familiarity with business analysis concepts
- 💻 Experience with Excel or statistical software
- 📚 Willingness to learn and apply advanced methods
- 📝 Open-mindedness to explore real-world applications

### What's inside

This course is packed with engaging modules that will guide you through advanced statistical concepts and practical applications.

- Laying the Groundwork: Advanced Statistical Concepts
- Data Collection Mastery: Transforming Raw Data into Insights
- Unlocking Insights: Advanced Statistical Analysis Techniques
- KPI Identification: Measuring Success with Precision
- Strategy Formulation: From Insights to Action
- Communicating Insights: The Power of Presentation

**Interactive Quizzes**

Each module includes quizzes designed to reinforce your understanding and application of the concepts learned, ensuring you are well-prepared for real-world challenges.

**Homework**

Throughout the course, you’ll engage in hands-on assignments that simulate real-world scenarios, including creating data collection plans and developing KPI dashboards.

**Practical Project**

Analyze a real-world business case using statistical methods to identify key performance indicators and develop actionable business strategies over 6 weeks.

**Before You Start**

Before you dive in, we’ll provide resources to refresh your knowledge and set you up for success!

**Books to Read**

Recommended readings will deepen your understanding and provide additional insights into business analytics and statistical methods.

**Glossary**

A glossary of key terms will be available to help you navigate the course content with ease.


### What will you learn

By the end of this course, you’ll be equipped with the skills necessary to excel in business analytics.

- Master advanced statistical tools for business analysis.
- Confidently identify and interpret key performance indicators (KPIs).
- Formulate actionable business strategies based on data insights.

**Time to complete this course: 6 weeks, with 10-15 hours of dedicated study per week.**


### Enroll Now and Transform Your Career!

A foundational grasp of statistics is crucial as it forms the backbone of advanced analysis. Familiarity with concepts like mean, median, mode, and standard deviation will help you grasp more complex statistical methods throughout the course.

Basic Understanding of Statistics

Understanding core business analysis principles is essential for applying statistical methods effectively. This knowledge will enable you to contextualize data insights within real-world business scenarios.

Familiarity with Business Analysis Concepts

Hands-on experience with tools like Excel or other statistical software is vital. You'll be using these tools to perform analyses, visualize data, and derive actionable insights during the course.

Experience with Excel or Statistical Software

## Unlock Your Potential with Advanced Business Analytics!
Embark on a transformative journey to master the art of business analytics through advanced statistical methods. This course empowers you to elevate your analytical skills, enabling you to make data-driven decisions that propel your organization to new heights. Prepare to dive deep into statistical tools, uncover key performance indicators, and develop actionable strategies that drive success in real-world business scenarios. Your journey will culminate in a practical project where theory meets practice, equipping you with the confidence to tackle complex business challenges head-on.

### Who is it For?
This course is designed for aspiring business analysts, data enthusiasts, and decision-makers who are eager to enhance their analytical skills.
- **Skill Level**: Intermediate
- **Audience**:
  - Aspiring business analysts looking to enhance their skills
  - Data enthusiasts wanting to leverage statistics in decision-making
  - Business managers aiming to make data-driven decisions

### Prerequisites
To ensure you get the most out of this course, a basic understanding of statistics, familiarity with business analysis concepts, and experience with Excel or statistical software are essential.
- Basic understanding of statistics
- Familiarity with business analysis concepts
- Experience with Excel or statistical software
- Willingness to learn and apply advanced methods
- Open-mindedness to explore real-world applications

### What's Inside?
This course is packed with engaging modules that will guide you through advanced statistical concepts and practical applications.
- **Modules**:
  - Laying the Groundwork: Advanced Statistical Concepts
  - Data Collection Mastery: Transforming Raw Data into Insights
  - Unlocking Insights: Advanced Statistical Analysis Techniques
  - KPI Identification: Measuring Success with Precision
  - Strategy Formulation: From Insights to Action
  - Communicating Insights: The Power of Presentation
- **Quizzes**: Each module includes quizzes designed to reinforce your understanding and application of the concepts learned, ensuring you are well-prepared for real-world challenges.
- **Assignments**: Throughout the course, you’ll engage in hands-on assignments that simulate real-world scenarios, including creating data collection plans and developing KPI dashboards.
- **Practical Project**: Analyze a real-world business case using statistical methods to identify key performance indicators and develop actionable business strategies over 6 weeks.
- **Before You Start**: Before you dive in, we’ll provide resources to refresh your knowledge and set you up for success!
- **Books to Read**: Recommended readings will deepen your understanding and provide additional insights into business analytics and statistical methods.
- **Glossary**: A glossary of key terms will be available to help you navigate the course content with ease.

### What Will You Learn?
By the end of this course, you’ll be equipped with the skills necessary to excel in business analytics.
- **Skills**:
  - Master advanced statistical tools for business analysis.
  - Confidently identify and interpret key performance indicators (KPIs).
  - Formulate actionable business strategies based on data insights.

### Time to Complete
6 weeks, with 10-15 hours of dedicated study per week.

### Enroll Now and Transform Your Career!

The application of statistical methods to analyze business data for informed decision-making.

Decisions made based on data analysis rather than intuition or personal experience.

Measurable values that demonstrate how effectively a company is achieving key business objectives.

Software and methods used to collect, analyze, and interpret data in business contexts.

A branch of mathematics dealing with the likelihood of different outcomes.

Statistical methods that summarize and describe data characteristics.

Techniques that allow conclusions to extend beyond the immediate data alone.

A statistical method for modeling the relationship between dependent and independent variables.

Analysis of variance; a statistical method used to compare means among three or more groups.

Techniques used to analyze time-ordered data points to identify trends and patterns.

The graphical representation of information and data to facilitate understanding.

The process of correcting or removing inaccurate records from a dataset.

Analyzing data sets to summarize their main characteristics, often using visual methods.

A statistical method to determine if there is enough evidence to reject a null hypothesis.

A subset of artificial intelligence that enables systems to learn from data and improve over time.

The process of defining a business's direction and making decisions on resource allocation.

Processes in which outputs of a system are circled back and used as inputs.

The systematic gathering of information for analysis and decision-making.

A visual display of key metrics used to monitor business performance.

The process of involving individuals or groups who may be affected by a business's decisions.

The process of creating visual aids to effectively communicate information.

Conclusions drawn from data analysis that can be acted upon to improve business outcomes.

The skills, technologies, practices for continuous iterative exploration, and investigation of past business performance.

The process of making sense of numerical data and drawing conclusions.

Working together with data professionals to enhance decision-making processes.

Understanding probability is essential for making informed business decisions. It allows you to quantify uncertainty, assess risks, and evaluate potential outcomes. In this lesson, we'll break down the key concepts of probability and illustrate how they can be applied in real-world business scenarios.

Probability is the measure of the likelihood that an event will occur. It ranges from 0 (impossible event) to 1 (certain event). Here are some fundamental concepts you should grasp:

**Random Variables:** Variables whose values are determined by the outcome of a random phenomenon.

**Probability Distributions:** Functions that describe the likelihood of different outcomes. Examples include normal, binomial, and Poisson distributions.

**Expected Value:** The long-term average value of a random variable, providing insight into the average outcome of a decision.

Importance of Probability in Decision-Making

In business, probability helps in assessing risks and making decisions under uncertainty. For instance, when launching a new product, probability can guide you in estimating the likelihood of success based on market research.

📊 **Tip:** Always consider the worst-case and best-case scenarios when using probability in decision-making. This helps in preparing for uncertainties.

Application of Probability in Risk Assessment

Risk assessment is a crucial part of business strategy. By applying probability, you can identify potential risks and their impacts. Here's a practical approach to integrating probability into risk assessment:

**Identify Risks:** List potential risks associated with a business decision.

**Estimate Probabilities:** Assign probabilities to each risk based on historical data or expert judgment.

**Calculate Impact:** Assess the potential impact of each risk on your business objectives.

**Develop Mitigation Strategies:** Create strategies to minimize the impact of high-probability risks.

To reinforce your learning, consider the following scenarios and apply probability concepts to each:

Evaluate the probability of a marketing campaign's success based on past performance data.

Assess the risks associated with investing in a new market and calculate the expected value of your investment.

Real-World Example: Product Launch Probability

Imagine you are launching a new product. By conducting market research, you find that:

70% of consumers are likely to purchase the product.

The cost of launching the product is $10,000.

Using this information, you can calculate the expected profit and make a data-driven decision about whether to proceed with the launch.

const probabilitySuccess = 0.7;
const expectedProfitPerSale = 50;
const launchCost = 10000;

const expectedSales = probabilitySuccess * (launchCost / expectedProfitPerSale);
const totalExpectedProfit = expectedSales * expectedProfitPerSale - launchCost;
console.log(`Total Expected Profit: $${totalExpectedProfit}`);

When applying probability in business decision-making, be mindful of these common pitfalls:

Overestimating probabilities based on anecdotal evidence.

Ignoring the impact of external factors that can influence outcomes.

Failing to update probabilities as new data becomes available.

Understanding and applying probability is essential for making informed business decisions. By mastering these concepts, you will enhance your analytical skills and be better equipped to navigate uncertainties.

Probability Homework

Rethinking Probability: The Backbone of Statistics

Probability Quiz

Descriptive statistics provide a way to summarize and visualize data, allowing analysts to understand the main features of a dataset. This includes measures such as mean, median, mode, and standard deviation, which help in interpreting the data at a glance.

🔍 Key Insight: Descriptive statistics are often the first step in data analysis, providing a foundation for deeper exploration.

On the other hand, inferential statistics allow analysts to make predictions or generalizations about a population based on a sample of data. This is crucial in business decision-making, where it is often impractical to collect data from an entire population.

🧠 Common Pitfall: Misusing inferential statistics by drawing conclusions from a sample that is not representative of the population can lead to poor decision-making.

Descriptive statistics should be used when you need to summarize data, while inferential statistics should be applied when you want to make predictions or test hypotheses. Knowing when to use each type is essential for effective data analysis.

Use descriptive statistics for: 
- Summarizing historical data 
- Creating visualizations (charts, graphs) 
- Reporting key metrics to stakeholders

Use inferential statistics for: 
- Making predictions based on sample data 
- Testing hypotheses about business strategies 
- Estimating population parameters from sample statistics

Understanding how to summarize data effectively is key to both descriptive and inferential statistics. Here are some common techniques:

Calculate measures of central tendency (mean, median, mode) to find the average.

Determine measures of variability (range, variance, standard deviation) to understand data spread.

Use visualizations (e.g., histograms, box plots) to present data visually.

To solidify your understanding, let's apply these concepts to a real dataset. You will perform both descriptive and inferential statistical analyses.

Choose a dataset relevant to your business interests.

Calculate descriptive statistics (mean, median, mode, standard deviation).

Perform an inferential analysis (e.g., hypothesis testing or confidence intervals).

Prepare a short report summarizing your findings and insights.

"Statistics is the grammar of science." - Karl Pearson.

Consider a retail business that wants to improve its sales strategy. By using descriptive statistics, they can analyze past sales data to identify trends. Then, they can apply inferential statistics to test hypotheses about the effectiveness of new marketing strategies.

Homework: Applying Descriptive and Inferential Statistics

Descriptive vs. Inferential Statistics: Understanding the Difference

Descriptive vs. Inferential Statistics Quiz

Statistical distributions are crucial for analyzing data and making informed business decisions. They describe how values are spread or distributed across a dataset, providing insights into patterns and trends. Understanding the characteristics of different distributions helps you choose the right statistical methods for your analysis.

The Normal distribution, often referred to as the bell curve, is one of the most important distributions in statistics. It is symmetric, with most observations clustering around the central peak, and it tapers off equally on both sides.

Characteristics of Normal Distribution:
- Symmetrical shape
- Mean, median, and mode are equal
- Approximately 68% of data lies within one standard deviation from the mean
- Useful for many statistical tests, including t-tests and ANOVA.

In business, the Normal distribution is often used to model phenomena such as heights, test scores, and other natural measurements. For example, if a company analyzes employee performance scores, they may find that scores follow a Normal distribution.

Binomial Distribution: Successes and Failures

The Binomial distribution applies to situations with two possible outcomes, such as success or failure. It is defined by two parameters: the number of trials (n) and the probability of success (p).

Characteristics of Binomial Distribution:
- Fixed number of trials
- Each trial has two possible outcomes
- The probability of success is constant across trials.

In a business context, the Binomial distribution can be used to model scenarios such as the number of defective products in a batch or the likelihood of customers making a purchase after receiving a marketing email.

The Poisson distribution is used for counting the number of events that occur in a fixed interval of time or space. It is characterized by the average number of events (λ) occurring in that interval.

Characteristics of Poisson Distribution:
- Describes the number of events in a fixed interval
- Events occur independently of each other
- The average rate (λ) is constant.

Businesses often utilize the Poisson distribution to model rare events, such as the number of customer complaints received in a day or the occurrence of equipment failures.

The Central Limit Theorem (CLT) states that the distribution of the sample means approaches a Normal distribution as the sample size increases, regardless of the shape of the population distribution. This principle is vital in business analytics because it allows analysts to make inferences about population parameters based on sample statistics.

💡 **Tip:** Understanding the Central Limit Theorem empowers you to apply statistical methods even when the underlying data distribution is unknown. This is particularly useful in business scenarios where data may not be perfectly Normal.

Practical Applications of Distributions in Business

By understanding these distributions, you can analyze business data more effectively. For instance, you can use the Normal distribution to assess employee performance, the Binomial distribution to evaluate marketing campaign success rates, and the Poisson distribution to predict customer service demands.

To solidify your understanding, conduct a hands-on exercise using a dataset of your choice. Identify the appropriate distribution for your data and analyze it.

Select a dataset relevant to your business context.

Determine which distribution (Normal, Binomial, or Poisson) best fits your data.

Perform the necessary analysis and summarize your findings.

When working with distributions, be cautious of the following:
- Assuming data follows a Normal distribution without testing it.
- Misinterpreting the implications of sample size on the Central Limit Theorem.
- Overlooking the context of your data when selecting a distribution.

"Statistics is the grammar of science." - Karl Pearson

In this lesson, you explored key statistical distributions and their applications in business contexts. Understanding Normal, Binomial, and Poisson distributions will enhance your analytical capabilities, allowing you to make data-driven decisions with confidence.

Homework: Distribution Application

Diving into Distributions: Normal, Binomial, and Poisson

Distribution Insights Quiz

Statistical significance is a foundational concept in data analysis that helps determine whether the results of your study are likely to be true or if they occurred by chance. Understanding this concept is crucial for making informed business decisions.

Understanding Null and Alternative Hypotheses

In hypothesis testing, we start by formulating two competing statements: the null hypothesis (H0) and the alternative hypothesis (H1). The null hypothesis typically asserts that there is no effect or no difference, while the alternative suggests that there is an effect or a difference. For example, if you're testing a new marketing strategy, H0 might state that the strategy has no impact on sales, while H1 would claim that it does.

🧠 **Tip:** Always define your hypotheses clearly before analyzing the data. This clarity will guide your analysis and interpretation.

Interpreting P-Values and Significance Levels

The p-value is a key metric in hypothesis testing. It measures the probability of observing the data, or something more extreme, if the null hypothesis is true. A common threshold for significance is 0.05. If your p-value is less than 0.05, you reject the null hypothesis, suggesting that your findings are statistically significant.

# Example of calculating a p-value in Python using SciPy
import scipy.stats as stats

# Sample data
sample_data = [2.3, 2.5, 2.8, 3.1, 3.0]

# Conduct a one-sample t-test
t_statistic, p_value = stats.ttest_1samp(sample_data, 2.5)
print(f'P-Value: {p_value}')

Conducting Hypothesis Tests in Business Contexts

To apply hypothesis testing effectively in business, consider a scenario where you want to determine if a new product line is more profitable than the existing one. Your null hypothesis might state that the average profit of the new line is equal to or less than the existing line, while the alternative would state that it is greater.

Hypothesis testing is not without its challenges. Common pitfalls include:
- Misinterpreting p-values: A low p-value does not imply that the effect is practically significant.
- Ignoring sample size: A small sample size can lead to unreliable results.
- Failing to consider the context: Always relate your findings back to the business context.

⚠️ **Common Mistake:** Don't confuse statistical significance with practical significance. A statistically significant result may not always translate to a meaningful business impact.

Hands-On Exercise: Hypothesis Testing Scenario

Now it's your turn to apply what you've learned! Consider a business scenario where you need to test a hypothesis. Follow these steps:
1. Define a business question that requires hypothesis testing.
2. Formulate your null and alternative hypotheses.
3. Collect data relevant to your question.
4. Conduct a hypothesis test using a statistical tool of your choice (e.g., Excel, R, Python).
5. Interpret your results and prepare to present your findings.

By engaging in this exercise, you'll gain practical experience in applying statistical methods to real-world business problems.

Homework: Applying Hypothesis Testing

Statistical Significance and Hypothesis Testing: Making Informed Decisions

Quiz: Testing Your Knowledge

Data visualization is more than just a trend; it's a crucial skill for business analysts. In today's data-driven environment, the ability to present data visually can significantly impact decision-making processes. This lesson will cover the importance of effective data visualization, various techniques and tools, and the principles of good design.

Importance of Data Visualization in Analytics

Data visualization serves as a bridge between raw data and actionable insights. It allows stakeholders to grasp complex concepts quickly and make informed decisions. According to research, people retain 65% of information when paired with visuals compared to only 10% when presented as text.

"Data is the new oil, but data visualization is the engine that drives it to decisions." - Unknown

Effective Visualization Techniques and Tools

Use charts and graphs to highlight trends and comparisons.

Incorporate infographics to summarize key points.

Utilize heat maps for geographic data representation.

Leverage tools like Tableau, Power BI, and Google Data Studio for interactive dashboards.

Principles of Good Design in Data Presentation

Good design is essential for effective data visualization. Here are some principles to keep in mind:

Keep it simple: Avoid clutter and focus on key messages.

Use color wisely: Colors should enhance understanding, not confuse.

Label clearly: Ensure all axes, legends, and titles are clearly labeled.

Tell a story: Each visualization should lead the viewer through a narrative.

Hands-On Exercise: Create Your Own Visualization

Now it's time to put your skills to the test. Select a dataset relevant to your business case and create a visualization that captures key insights. Use any of the tools discussed and apply the design principles you've learned.

Select a dataset and create a visualization.

Apply at least three different visualization techniques.

Prepare a brief explanation of your visualization choices.

🚫 **Common Mistakes in Data Visualization**

Real-World Example: Successful Data Visualization

Consider the case of a major retail chain that used data visualization to analyze customer purchasing patterns. By implementing heat maps, they identified peak shopping times and adjusted staffing accordingly, resulting in a 15% increase in customer satisfaction.

Mastering data visualization is a critical step in your journey as a business analyst. By effectively communicating data insights through visual storytelling, you can drive better decision-making and contribute to your organization's success.

Homework: Data Visualization Project

The Power of Data Visualization: Telling Stories with Statistics

Data Visualization Quiz

Understanding how to apply statistical concepts in real-world scenarios is crucial for business analysts. This lesson focuses on bridging the gap between theory and practice, equipping you with the skills to analyze data effectively and derive meaningful insights.

Synthesizing Statistical Concepts into Practical Applications

To effectively apply statistical concepts, it’s essential to understand their relevance to specific business problems. Begin by identifying a business issue that can be addressed through statistical analysis. For example, a retail company may want to understand customer purchasing behavior to optimize inventory.

🔍 **Tip:** Focus on real business problems that require data-driven solutions. This will help you select the right statistical methods and tools for your analysis.

Case Studies of Successful Statistical Applications in Business

Case studies provide valuable insights into how statistical methods are applied in real-world scenarios. For instance, a case study on a marketing campaign could illustrate how A/B testing was used to determine the most effective advertisement.

Once you have analyzed the data, the next step is to develop actionable insights. This involves interpreting the results in the context of the business problem. For instance, if your analysis reveals that a specific demographic is more likely to purchase a product, you can recommend targeted marketing strategies.

Identify a business case and outline how you would apply statistical concepts to analyze it.

Hands-On Exercise: Real-World Application

Select a real-world dataset related to a business case you are interested in. Conduct exploratory data analysis (EDA) to uncover patterns and insights. Use statistical methods such as regression analysis or hypothesis testing to derive actionable insights.

Choose a dataset relevant to your business case.

Conduct exploratory data analysis (EDA) to identify trends.

Apply statistical methods to analyze the data.

Summarize your findings and develop actionable insights.

When applying statistical methods, be mindful of common pitfalls such as:
- Misinterpreting correlation as causation
- Ignoring data quality and sources
- Failing to document your analysis process

⚠️ **Common Mistake:** Avoid jumping to conclusions without thorough analysis. Always validate your findings with additional data.

Bridging the gap between statistical theory and practice is essential for effective business analysis. As you move forward, focus on applying these concepts to real-world scenarios, and continuously seek feedback to refine your analytical skills.

Homework: Real-World Application of Statistical Concepts

Bridging Theory and Practice: Real-World Applications of Statistical Concepts

Applying Statistical Concepts

Dive into the foundational theories that underpin advanced statistical methods. This module challenges you to rethink your understanding of statistics and equips you with essential tools to analyze business data effectively.

Laying the Groundwork: Advanced Statistical Concepts

Reliable data sources are the backbone of any successful business analysis. Without trustworthy data, your insights may lead to misguided strategies and decisions. This lesson will help you navigate through the complex world of data sourcing, ensuring that the data you use is accurate, relevant, and actionable.

Criteria for Assessing Data Source Reliability

To evaluate the reliability of a data source, consider the following criteria:

Authority: Is the source credible and recognized in the field?

Accuracy: Is the data presented free from errors and verified?

Objectivity: Is the data presented without bias?

Coverage: Does the data cover the necessary scope for your analysis?

🔍 **Tip:** Always cross-reference data from multiple sources to ensure reliability!

Understanding the types of data sources can help you choose the right one for your analysis. Here are the main categories:

Primary Data: Data collected firsthand through surveys, interviews, or experiments.

Secondary Data: Data collected from existing sources, such as reports, articles, or databases.

Open Data: Publicly available datasets that can be used for analysis.

Commercial Data: Data purchased from providers that often offer specialized insights.

Data quality directly influences the accuracy of your analysis. Poor quality data can lead to misleading insights, which can ultimately harm business decisions. Here are some key aspects to consider regarding data quality:

Completeness: Is all necessary data present?

Consistency: Is the data consistent across different sources?

Timeliness: Is the data available when needed?

Relevance: Does the data align with your analysis objectives?

⚠️ **Common Mistake:** Relying on a single source of data can lead to skewed results. Always validate your data with multiple sources!

Hands-On Exercise: Evaluating Data Sources

Now, let's put your skills to the test! Choose a data source relevant to your business case and evaluate it using the criteria discussed.

Evaluate its reliability based on the criteria provided.

Document your findings and be prepared to discuss them in the next lesson.

Identifying Reliable Data Sources

Learn the art of data collection and preparation. This module emphasizes the significance of data quality and equips you with techniques to ensure your datasets are ready for analysis.

Quick Navigation

BUSINESS STATISTICS#1

DATA-DRIVEN DECISIONS#2

KEY PERFORMANCE INDICATORS (KPIs)#3

STATISTICAL TOOLS#4

PROBABILITY THEORY#5

DESCRIPTIVE STATISTICS#6

INFERENTIAL STATISTICS#7

REGRESSION ANALYSIS#8

ANOVA#9

TIME SERIES ANALYSIS#10

DATA VISUALIZATION#11

DATA CLEANING#12

EXPLORATORY DATA ANALYSIS (EDA)#13

HYPOTHESIS TESTING#14

MACHINE LEARNING#15

STRATEGIC PLANNING#16

FEEDBACK LOOPS#17

DATA COLLECTION#18

KPI DASHBOARD#19

STAKEHOLDER ENGAGEMENT#20

PRESENTATION DESIGN#21

ACTIONABLE INSIGHTS#22

BUSINESS ANALYTICS#23

DATA INTERPRETATION#24

COLLABORATION WITH DATA TEAMS#25