Data Science Mastery:10-in-1 Data Interview Projects showoff from Udemy

Project 1: Exploratory Data Analysis Dive deep into the world of data exploration and visualization. Learn how to clean, preprocess, and draw meaningful insights from your datasets.

Project 2: Sentiment Analysis Uncover the underlying sentiments in text data. Master natural language processing techniques to classify text as positive, negative, or neutral.

Project 3: Predictive Modeling Predict the future today. Learn how to train machine learning models, evaluate their performance, and use them for future predictions.

Project 4: Time Series Analysis Step into the realm of time series data analysis. Learn how to preprocess and visualize time series data and build robust forecasting models.

Project 5: Big Data Analytics Scale up your data science skills with big data analytics. Learn how to process large datasets using Apache Spark in a distributed computing environment.

Project 6: Tabular Playground Series Analysis Unleash the power of data analysis as you dive into real-world datasets from the Tabular Playground Series. Learn how to preprocess, visualize, and extract meaningful insights from complex data.

Project 7: Customer Churn Prediction Harness the power of machine learning to predict customer churn and develop effective retention strategies. Analyze customer behavior, identify potential churners, and take proactive measures to retain valuable customers.

Project 8: Cats vs Dogs Image Classification Enter the realm of computer vision and master the art of image classification. Train a model to distinguish between cats and dogs with remarkable accuracy.

Project 9: Fraud Detection Become a fraud detection expert by building a powerful machine learning model. Learn anomaly detection techniques, feature engineering, and model evaluation to uncover hidden patterns and protect against financial losses.

Project 10: Houses Prices Prediction Real estate is a dynamic market, and accurate price prediction is vital. Develop the skills to predict housing prices using machine learning algorithms.

Enroll now and start your journey towards becoming a proficient data scientist. Unlock the power of data and transform your career.

What's inside

Learning objectives

Students will learn how to preprocess, visualize, and extract meaningful insights from complex datasets, enhancing their data analysis skills.
Students will gain the ability to train machine learning models, evaluate their performance, and use them for future predictions, thereby mastering predictive m
Through sentiment analysis, students will master natural language processing techniques to classify text as positive, negative, or neutral.
Students will learn how to preprocess and visualize time series data and build robust forecasting models, gaining proficiency in time series analysis.

Students will scale up their data science skills with big data analytics, learning how to process large datasets using apache spark in a distributed computing.
Students will apply ml to real-world problems, such as customer churn prediction, image classification, fraud detection, and housing price prediction.
By working on ten hands-on projects, students will build a portfolio that showcases their skills and experience, making them industry-ready.
With the practical experience gained from this course, students will be well-prepared to transform their careers in the field of data science and ml.

Students will learn how to preprocess, visualize, and extract meaningful insights from complex datasets, enhancing their data analysis skills.
Students will gain the ability to train machine learning models, evaluate their performance, and use them for future predictions, thereby mastering predictive m
Through sentiment analysis, students will master natural language processing techniques to classify text as positive, negative, or neutral.
Students will learn how to preprocess and visualize time series data and build robust forecasting models, gaining proficiency in time series analysis.
Students will scale up their data science skills with big data analytics, learning how to process large datasets using apache spark in a distributed computing.
Students will apply ml to real-world problems, such as customer churn prediction, image classification, fraud detection, and housing price prediction.
By working on ten hands-on projects, students will build a portfolio that showcases their skills and experience, making them industry-ready.
With the practical experience gained from this course, students will be well-prepared to transform their careers in the field of data science and ml.

Syllabus

Introduction

Exploratory Data Analysis.

1. Visual Exploring of Google App Store Data.

2. Data Cleaning and Preprocessing of Google App Store Data.

3. Data Visualization Techniques.

4. Statistical Analysis and Hypothesis Testing.

5. Data Storytelling.

6. Conclusion.

The First Assignment for Project 1: Google App Store Data EDA.

Sentiment Analysis.

1. Introduction to Sentiment Analysis & NLP.

2. Text Preprocessing for Sentiment Analysis.

3. Feature Extraction for Sentiment Analysis.

4. Building Sentiment Analysis Models.

5. Evaluation of Sentiment Analysis Models.

Predictive Modeling.

1. Introduction to Predictive Modeling and Machine Learning.

2. Data Exploration and Preprocessing of the Titanic Dataset.

3. Model Selection and Evaluation of The Titanic Dataset.

4. Model Training and Hyperparameter Tuning of The Titanic Dataset.

5. Deployment of The Predictive Models of The Titanic Dataset.

Assignment For The Titanic Predictive Modeling Project.

Time Series Analysis.

1. Introduction.

2. Data Preprocessing and Cleaning.

3. Visualizing Time Series Data.

4. Building and Evaluating Forecasting Models.

5. Predicting Future Bitcoin Prices.

Big Data Analytics.

1. Introduction to Big Data Analytics and Apache Spark.

2. Big Data Data Exploration and Preprocessing.

3. Big Data Transformation and Feature Engineering.

4. Big Data Visualization and Analysis.

5. Conclusion and Next Steps.

Tabular Playground Series Analysis.

1. Reading and Preprocessing Data.

2. Data Transformation and Visualization.

3. Train-Test Split and Model Selection.

4. Model Training with XGBoost.

5. Making Predictions and Submission.

Customer Churn Prediction.

1. Introduction to Customer Churn Prediction.

2. Feature Selection and Model Building.

3. Advanced Techniques for Churn Prediction.

4. Ensemble Methods and Model Evaluation.

5. Model Interpretation, Deployment, and Next steps.

Cats vs Dogs Image Classification.

1. How to download Kaggle data in Google Collab?!

2. Creating Directories & The images data.

3. Image data preprocessing and visualization with Python.

4. Creating and Validating Model using CNN.

Fraud Detection.

1. Introducing Fraud Detection and Conducting Exploratory Data Analysis.

2. Model Building for Fraud Detection.

3. Advanced Techniques for Fraud Detection.

4. Model Evaluation and Interpretability.

5. Model Deployment.

Houses Prices Prediction.

1. Introduction to House Prices Prediction.

2. Housing Data Processing & Cleaning For ML Model.

3. Doing EDA (Exploratory Data Analysis) Using Data Visualization.

4. Building Model for the Housing Data.

5. Validating Our Model.

Bonus

Thank you.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Offers hands-on experience with ten distinct data science projects, which allows learners to immediately apply their knowledge and build a professional portfolio

Covers a wide range of data science applications, including sentiment analysis, predictive modeling, and image classification, which provides a comprehensive overview of the field

Includes a project on big data analytics using Apache Spark, which is essential for handling large datasets in real-world scenarios

Features projects using real-world datasets from the Tabular Playground Series and Kaggle, which provides practical experience with industry-relevant data

Emphasizes model deployment, which is a crucial step in the data science pipeline often overlooked in introductory courses

Focuses on the Titanic dataset for predictive modeling, which may be somewhat dated, as more current datasets are available for demonstrating these techniques

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Data Science Mastery:10-in-1 Data Interview Projects showoff with these activities:

Review Statistics Fundamentals

Show steps

Reinforce your understanding of statistical concepts like hypothesis testing and distributions, which are crucial for exploratory data analysis and predictive modeling projects.

Browse courses on Statistical Analysis

Show steps

Review key statistical concepts and formulas.
Work through practice problems on hypothesis testing.
Familiarize yourself with common probability distributions.

Read 'Python Data Science Handbook'

Show steps

Supplement your understanding of Python data science tools and techniques, which are essential for completing the projects in this course.

View Python Data Science Handbook: Essential Tools... on Amazon

Show steps

Read the chapters relevant to the current project.
Experiment with the code examples provided in the book.
Apply the techniques learned to your own datasets.

Complete Kaggle Challenges

Show steps

Sharpen your skills in data preprocessing, feature engineering, and model building by participating in Kaggle competitions related to the course projects.

Show steps

Select a Kaggle competition related to a course project.
Download the dataset and explore the data.
Build and evaluate a machine learning model.
Submit your predictions and analyze your performance.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Read 'Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow'

Show steps

Enhance your understanding of machine learning algorithms and frameworks, which are essential for building predictive models in this course.

View Hands-On Machine Learning with Scikit-Learn,... on Amazon

Show steps

Read the chapters relevant to the current project.
Experiment with the code examples provided in the book.
Apply the techniques learned to your own datasets.

Write a Blog Post on a Project

Show steps

Solidify your understanding of a specific project by explaining the problem, approach, and results in a blog post.

Show steps

Choose a project from the course to write about.
Outline the key steps and findings of the project.
Write a clear and concise blog post explaining the project.
Publish your blog post on a platform like Medium or your personal website.

Expand on a Course Project

Show steps

Deepen your expertise by extending one of the course projects with additional features, datasets, or algorithms.

Show steps

Select a course project to expand upon.
Identify areas for improvement or extension.
Implement the new features or algorithms.
Evaluate the performance of the expanded project.

Contribute to a Data Science Library

Show steps

Deepen your understanding of data science tools and techniques by contributing to open-source libraries like Pandas, Scikit-Learn, or TensorFlow.

Show steps

Identify a bug or feature request in an open-source library.
Fork the repository and create a new branch.
Implement the fix or feature.
Submit a pull request with your changes.

Career center

Learners who complete Data Science Mastery:10-in-1 Data Interview Projects showoff will develop knowledge and skills that may be useful to these careers:

Data Scientist

A data scientist is a professional who uses programming, statistical methods, and machine learning to extract actionable knowledge and value from data. This course directly aligns with the role of a data scientist. The hands-on projects in exploratory data analysis, predictive modeling, and machine learning provide a practical foundation for this career. A data scientist needs to be comfortable with preprocessing data, building models, and evaluating their performance, all of which are covered by this course. The projects that focus on real-world problems such as customer churn prediction and fraud detection further prepare you for common challenges faced in the field. This course provides a strong foundation to becoming a data scientist.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

A machine learning engineer focuses on the practical aspects of developing, deploying, and maintaining machine learning systems. This role is highly suitable for someone completing this course. The course's curriculum, including projects in predictive modeling, image classification, and fraud detection, builds the necessary skills to work as a machine learning engineer. The course gives you a chance to train machine learning models and evaluate their performance, core responsibilities of machine learning engineers. Additionally, the work with real data sets in the projects strengthens relevant practical skills. The course's emphasis on building a practical portfolio makes it a useful stepping stone to a career as a machine learning engineer.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

A data analyst uses data to identify trends, patterns, and insights to support decision making within an organization. This course may be useful for someone seeking a career as a data analyst. The course provides crucial skills in data exploration, cleaning, preprocessing, and visualization, which are all foundational for a data analyst. The project based learning approach, which includes sentiment analysis, time series analysis, and customer churn prediction, mirrors the typical tasks of a data analyst. The course helps build a foundation in statistical analysis and extracting meaningful insights from data, all crucial skills for a data analyst. The projects give you the skills to dive deep into data.

See salaries and explore the career path for Data Analyst

Business Intelligence Analyst

A business intelligence analyst interprets data to provide insights and recommendations to improve business performance. This course may be useful for a business intelligence analyst. The course's focus on data analysis and visualization may help build a foundation useful for this role. The course's exercises in exploratory data analysis will help an aspiring business intelligence analyst to make sense of complex datasets. Furthermore, the course’s projects, including those on customer churn prediction and the tabular playground series, mimic the work a business intelligence analyst may need to perform, involving real-world data sets. This course may provide relevant skills in data handling and statistical analysis.

See salaries and explore the career path for Business Intelligence Analyst

Financial Analyst

A financial analyst provides guidance to businesses and individuals making investment decisions by analyzing financial data. This course may be useful for a financial analyst. The course’s work in predictive modeling and time series analysis may build a foundation for the quantitative skills needed for this role. The practical projects, such as housing price predictions, provide experience in forecasting financial trends. The course’s focus on data analysis and extracting insights may be helpful for a financial analyst who uses data to guide strategic decision making. For a financial analyst, this course may provide skills to better analyze markets.

See salaries and explore the career path for Financial Analyst

Research Scientist

A research scientist conducts experiments and analyzes data to advance scientific knowledge, often requiring advanced degrees. This course may be useful for a research scientist. The course's project based learning in data analysis, predictive modeling, and machine learning may help build foundational skills. The techniques taught in sentiment analysis may be useful for analyzing qualitative data. The work in time series analysis may be useful in quantitative research. The course provides experience working with real world data, which will help someone interested in a research career. A research scientist needs to be skilled and comfortable with data analysis.

See salaries and explore the career path for Research Scientist

Statistician

Statisticians collect, analyze, and interpret data to solve problems through statistical methods, often requiring advanced degrees. This course may be useful for an aspiring statistician. The projects involving exploratory data analysis and predictive modeling will help build a foundation for the statistical methods needed in this role. The course's focus on hypothesis testing provides some direct experience with statistical analysis. The projects included, such as time series analysis and customer churn prediction, will introduce students to practical applications of statistical methods. For a statistician, this course will help build a statistical foundation.

See salaries and explore the career path for Statistician

Quantitative Analyst

A quantitative analyst, often referred to as a quant, develops and implements mathematical and statistical models for financial markets. This course may be useful for someone interested in becoming a quantitative analyst. The course's content in predictive modeling and time series analysis builds some necessary skills for this job. The assignments in the course, including housing price prediction, may be relevant to financial forecasting which a quantitative analyst performs. The course helps build skills in data analysis and model building, which are important in this role. This course may be useful to help build a strong background for a quantitative analyst.

See salaries and explore the career path for Quantitative Analyst

Market Research Analyst

A market research analyst studies market conditions to examine potential sales of a product or service. This course may be useful for a market research analyst, because the course projects include customer churn prediction, which is a very important component of market research. The course’s focus on data analysis and statistical modeling can be applied to market data. The course may help build skills in identifying patterns in data. The training and experience in sentiment analysis will be helpful in assessing the public’s perception about a good or a service, which is a major task for market research analysts. The course may help one understand how to best analyze data for market research.

See salaries and explore the career path for Market Research Analyst

Risk Analyst

A risk analyst assesses and analyzes potential risks and develops strategies to mitigate them, often working in finance or insurance. This course may be useful for someone who wants to work as a risk analyst. The course's project in fraud detection is directly relevant to a risk analyst. The predictive modeling skills learned in this course may help one to forecast and anticipate potential risks for a firm. The focus on data analysis may help a risk analyst better assess potential risks for an organization. This course provides some useful skills for someone working in risk analysis.

See salaries and explore the career path for Risk Analyst

Operations Research Analyst

An operations research analyst uses mathematical and analytical methods to help organizations make better decisions. This course may be useful to someone interested in operations research. The predictive modeling skills learned in this course may be useful in operations research. The various projects may help a student better understand how to build models for various scenarios. The experience with data analytics may prove useful for an operations research analyst. This course may be useful for one who wants to work in operations research.

See salaries and explore the career path for Operations Research Analyst

Software Engineer

A software engineer develops, tests, and maintains software systems, and this course may be helpful for one who wants to be a software engineer in the field of data science. The course provides practical skills in using Python, which is a frequently used language in software engineering. The course includes projects on machine learning and data analysis which will prove useful in developing data pipelines. The course’s focus on model development and deployment will help one understand the lifecycle of a software application in the data science space. The course may help an aspiring software engineer in the field of data science.

See salaries and explore the career path for Software Engineer

Database Administrator

A database administrator is responsible for managing and maintaining databases, ensuring the data is organized and accessible. This course may be useful for someone who wants to be a database administrator because it covers data handling and analysis. The projects in this course, particularly the one focused on big data analytics, may give one a basic understanding of how large data sets are managed. The course’s focus on data cleaning and preprocessing can be helpful for understanding databases. The course may provide useful skills for a database administrator who needs to manage large datasets.

See salaries and explore the career path for Database Administrator

Systems Analyst

A systems analyst evaluates and improves computer systems. This course may be useful to a systems analyst who is working within an organization that relies on data driven decision-making. The projects focus on data analysis and machine learning can assist a systems analyst in gaining a more robust understanding of various data driven systems. The work in this course with big data analytics may improve a systems analyst’s ability to handle system infrastructure where big data is concerned. The course may prove helpful to one wishing to work as a systems analyst in the data science field.

See salaries and explore the career path for Systems Analyst

Project Manager

A project manager plans, oversees, and executes projects from start to finish. This course may be useful for a project manager working on projects in the data science field. The course provides a solid grounding in data analysis pipelines, machine learning, and big data handling, which can be useful to managing projects related to these areas. The course's use of real world projects gives one an authentic preview of projects within this field. The course may help project managers who must manage projects in the realm of data science.

See salaries and explore the career path for Project Manager

Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Data Science Mastery:10-in-1 Data Interview Projects showoff.

Python Data Science Handbook

Save

Provides a comprehensive overview of essential Python libraries for data science, including NumPy, Pandas, Matplotlib, and Scikit-Learn. It is particularly useful for the exploratory data analysis, predictive modeling, and image classification projects in this course. The book offers practical examples and clear explanations, making it an excellent reference for both beginners and experienced data scientists. It is commonly used as a textbook in data science courses.

Python Data Science Handbook: Essential Tools for...

Paperback

$$$

Hands-On Machine Learning with Scikit-Learn, Keras,...

Save

Provides a practical guide to machine learning using Scikit-Learn, Keras, and TensorFlow. It covers a wide range of topics, including classification, regression, clustering, and deep learning. It is particularly useful for the predictive modeling, customer churn prediction, image classification, and fraud detection projects in this course. This book is commonly used as a textbook at academic institutions and by industry professionals.

Hands-On Machine Learning with Scikit-Learn, Keras,...

Paperback

Hands-On Machine Learning with Scikit-Learn, Keras,...

Paperback

$$$

Hands-On Machine Learning with Scikit-Learn, Keras,...

Kindle Edition

Data Science Mastery

10-in-1 Data Interview Projects showoff

What's inside

Learning objectives

Syllabus

Good to know

Save this course

Activities

Career center

Reading list

Share

Similar courses