Statistical Foundations for Data Science and Analytics with Python

What you will learn:

Master the calculation and interpretation of core descriptive statistics such as mean, median, and standard deviation for comprehensive data summaries.
Skillfully apply probability rules and Bayes’ Theorem to effectively solve complex conditional probability challenges in various scenarios.
Develop proficiency in analyzing and summarizing datasets using Python, including computing statistical measures and creating impactful data visualizations.
Formulate precise null and alternative hypotheses, then conduct rigorous one-sample Z and T-tests to evaluate claims about population means.
Gain expertise in applying descriptive statistics, including mean, median, mode, and standard deviation, to succinctly summarize any given dataset.
Accurately calculate and interpret conditional probability, and leverage the powerful Bayes' Theorem to tackle real-world analytical problems.
Effectively model diverse real-world situations by employing key probability distributions, including Binomial, Poisson, and Normal distributions.
Attain a deep understanding and ability to articulate the core concepts of statistical inference and the foundational Central Limit Theorem.
Confidently perform hypothesis testing, specifically T-tests, using Python to make informed, data-driven decisions and validate analytical results.

Description

Are you striving to transition from merely observing data to actively driving strategic decisions backed by robust quantitative evidence? If you recognize that a thriving career in Data Science, Business Intelligence, or Advanced Analytics necessitates a deeper grasp than just basic averages, then this course is your definitive pathway to building that indispensable statistical bedrock.

Command the Quantitative Underpinnings of Data Science and Business Insights

This is precisely the immersive, hands-on learning experience you’ve been seeking. We meticulously crafted this program with a singular objective: to equip you with the practical competencies to confidently manipulate data and derive credible statistical inferences.

Upon successful completion of this program, you will be proficient in:

Constructing a robust understanding of descriptive statistics, including central tendency and measures of spread.
Applying fundamental probability principles, such as conditional probability and the powerful Bayes' Theorem.
Interpreting and utilizing key probability distributions like Binomial, Poisson, and the Normal distribution.
Executing real-world hypothesis tests, including T-tests, to empirically validate business hypotheses with data.

Why is Statistical Proficiency Your Ultimate Professional Advantage?

In the contemporary landscape, data is undeniably a critical asset. However, raw data alone possesses limited utility. The true value resides in the actionable intelligence extracted from it. Leading organizations globally, from tech giants like Google and Netflix to e-commerce powerhouses like Amazon, rely on sophisticated statistical models to underpin their critical decision-making processes. If your career aspirations lie in data-centric roles, mastering the language of statistics is absolutely paramount.

This program acts as your essential interpreter. It effectively bridges the chasm between being a 'Data Consumer' (someone who merely views dashboards) and a 'Data Strategist' (an individual who can construct, analyze, and critically question them). We ensure you gain both the conceptual clarity and the essential Python programming capabilities to engage with data confidently, ethically, and responsibly.

Our Pedagogical Approach (Your Practical Skillset)

We firmly believe that genuine statistical comprehension is achieved through active engagement and practical application. Beginning with 'Introduction to Data and Variables,' we will systematically build your knowledge base, module by module, in a logical progression.

Clarity & Simplicity: We have deconstructed intricate concepts such as Bayes' Theorem, the Central Limit Theorem, and p-values into easily digestible, step-by-step explanations.
Real-World Relevance: Our focus is squarely on practical implementation rather than abstract theoretical discussions. We utilize concrete, real-world case studies to explore common challenges like sampling bias, effect sizes, and the inherent limitations of statistical tests, ensuring you evolve into an effective and ethically sound data analyst.

You will cultivate the abilities to tackle data quality issues, identify and manage outliers, and address missing values. You'll learn to meticulously construct and accurately interpret confidence intervals, and competently execute both one-sample and two-sample T-tests to rigorously test actual business hypotheses.

Are you prepared to embark on your data science odyssey armed with an unshakeable statistical foundation?

Enroll today, explore the complimentary preview lectures, and commence developing the highly sought-after quantitative competencies that top employers demand!

Course proudly delivered by MTF Institute of Management, Technology and Finance

MTF is a distinguished global educational and research institution headquartered in Lisbon, Portugal. It specializes in hybrid (on-campus and online) business and professional education across various domains including Business & Administration, Science & Technology, and Banking & Finance.

The MTF R&D center is dedicated to cutting-edge research in Artificial Intelligence, Machine Learning, Data Science, Big Data, WEB3, Blockchain, Cryptocurrency & Digital Assets, Metaverses, Digital Transformation, Fintech, Electronic Commerce, and the Internet of Things.

MTF maintains official partnerships with industry leaders such as IBM, Intel, and Microsoft, and is a proud member of the Portuguese Chamber of Commerce and Industry.

With a global reach across 218 countries, MTF has been the educational choice for over 915,000 students worldwide.

Meet Your Instructor:

Dr. Alex Amoroso is an accomplished professional with an extensive background spanning academia and industry. Her expertise lies in research methodologies, strategic planning, and product innovation. Holding a Doctorate Degree from the School of Social Sciences and Politics in Lisbon, Portugal, where her exceptional research earned her distinction and honor, Dr. Amoroso brings profound knowledge and practical insights to her teaching.

Beyond her doctoral achievements, Ms. Amoroso has served as an invited educator, delivering courses to a diverse student body ranging from undergraduates to business professionals and executives. Currently, she spearheads the Product Development academic domain as Head of the School of Business and Management at MTF. At EIMT, she further contributes by supervising doctoral students and providing advanced instruction in research design and methodologies. Additionally, she offers her expertise as a Research Consultant.

Seamlessly blending her academic rigor with practical business acumen, Ms. Amoroso has achieved remarkable success in her corporate career, leading R&D initiatives, product development, strategic growth, and market analysis across a wide spectrum of companies. She has implemented best market practices in sectors including Banking and Finance, PropTech, Consulting and Research, and innovative Startups.

Alex Amoroso's significant scientific contributions include numerous publications in esteemed journals, alongside oral presentations and poster sessions at international conferences. Her research findings have been showcased at prestigious institutions such as the School of Political and Social Sciences and the Stressed Out Conference at UCL, among others.

Driven by a passion for interdisciplinary collaboration and a steadfast commitment to fostering positive change, Alex Amoroso is dedicated to empowering learners and professionals to leverage cutting-edge methodologies for achieving excellence in the global business landscape.

Curriculum

Introduction

Begin your journey into the world of data science with a warm welcome and a comprehensive overview of what you'll achieve in the Probability and Statistics course. This section sets the stage, introducing you to the course structure, learning objectives, and the MTF Institute's commitment to your educational success.

Module 1: Descriptive Statistics and Data Handling

This foundational module introduces learners to the fundamental concepts of data and variables, exploring different types of data. You'll delve into essential descriptive statistics such as measures of central tendency (mean, median, mode) and dispersion (variance, standard deviation) to effectively summarize datasets. The module also covers critical data visualization techniques, an introduction to using Python for data summarization, and crucial aspects of data quality, ensuring a robust understanding of handling and interpreting raw information.

Module 2: Probability Foundations

Dive into the core principles of probability, starting with basic counting techniques essential for understanding likelihood. This module thoroughly explains the axioms of probability and fundamental rules, progressing to more complex topics like conditional probability and the concept of statistical independence. A dedicated lesson then guides you through the application of Bayes’ Theorem, a powerful tool for updating probabilities based on new evidence.

Module 3: Random Variables and Probability Distributions

Explore the crucial concepts of random variables, differentiating between discrete and continuous types, and learn to calculate their expectation and variance. This module then introduces common discrete probability distributions (like Binomial and Poisson) and continuous distributions, culminating in a detailed practical application of the Normal Distribution, a cornerstone of statistics, helping you model real-world phenomena.

Module 4: Sampling and Estimation

Understand the critical distinction between populations and samples, and master various sampling methods while learning to identify and mitigate sampling bias. This module delves into sampling distributions and the pivotal Central Limit Theorem (CLT), which underpins statistical inference. You'll then learn about point estimation and how to construct and interpret interval estimations, specifically confidence intervals (CIs), concluding with a vital discussion on statistical versus practical significance.

Module 5: Introduction to Hypothesis Testing

This module introduces the fundamental principles and systematic steps of hypothesis testing, a cornerstone of data-driven decision-making. You will learn to formulate null and alternative hypotheses and perform one-sample tests. The course then progresses to cover two-sample and paired t-tests for comparing means. Key concepts like statistical power and effect size are explained, preparing you to effectively communicate and report statistical results, and providing a bridge to more advanced topics like regression analysis.

Next Steps

Conclude your comprehensive statistics journey with a bonus section designed to guide you on your next steps in data science and analytics. This brief but valuable lecture provides insights and recommendations for continued learning and application of your newly acquired statistical expertise.

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