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Unlocking Real World Data Science Practical Projects with Python in 2023

Develop real world data science practical projects in the fields of Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and Time Series analysis to enhance your skills and increase your chances of securing a job as a Data Scientist or ML Engineer.

Created by: Shan Singh

What you’ll learn in real world data science

  • Gain hands-on practical experience in diverse domains of Data Science, such as Machine Learning, Natural Language Processing, and Time Series Analysis, through real-world project work.
  • The ability to develop Natural Language Processing models specifically designed for analyzing customer sentiments.
  • Proficiency in creating time series forecasting models to accurately predict stock prices.
  • Skills in effectively mapping your business or research problems into Data Science problems.
  • Knowledge of best practices for handling and analyzing real-world data sets.
  • The opportunity to apply all the acquired skills and knowledge through Data Science Capstone Projects.
real world data science

Course Content:

  1. Introduction to the Course
    • Overview of Data Science and its core objectives.
    • Participants will gain an in-depth understanding of the life cycle involved in a Data Science project.
  2. Project 1: Predict Fare of Airline Tickets using Machine Learning
    • Understanding the problem statement and its significance.
    • Gathering and preprocessing the airline ticket data.
    • Conducting Exploratory Data Analysis (EDA) to extract meaningful insights from the data.
    • Feature engineering and selection.
    • Building and training Machine Learning models for fare prediction.
    • Evaluating and fine-tuning the models.
    • Presenting the results and drawing conclusions.
  3. Project 2: Predict Password Strength using Natural Language Processing
    • Exploring the problem of password strength prediction.
    • Preparing and cleaning the password dataset.
    • Feature extraction from passwords.
    • Building a Natural Language Processing model for password strength classification.
    • Evaluating and improving the model’s performance.
    • Discussing the implications of the results.
  4. Project 3: Forecasting Stock Prices using Time Series Analysis
    • An introductory exploration of time series analysis and its practical use cases.
    • Gathering historical stock price data.
    • Preprocessing and visualizing the data.
    • Building time series forecasting models, such as ARIMA or LSTM.
    • Evaluating and fine-tuning the models.
    • Predicting future stock prices and analyzing the results.
  5. Bonus Section
    • Additional topics, tips, or advanced techniques related to Data Science.
    • Optional projects or exercises for further practice and exploration.

Note: The course may also include supplementary materials such as lecture notes, code examples, datasets, and resources for further learning.

Requirements

  • Basic knowledge of programming is recommended, particularly in Python.
  • However, if you don’t have prior programming experience, there is a Basics of Python Course available for free, which you can follow to gain the necessary skills.
  • The course is designed to be accessible to anyone with basic programming knowledge, so there are no specific prerequisites.
  • Students who enroll in this course will acquire proficiency in data science concepts and methodologies, allowing them to directly apply these skills to solve real-world business problems.

Description


Here are some testimonials from previous students:

  1. He is very awesome! He explained all the concepts very well and his teaching and execution were excellent. I greatly appreciate this incredible course. Thanks a lot!” – Aravindan R
  2. The practical utility of the learning experience has been well taken care of, which ensures that the course becomes an interesting one!” – Sangita Bhadra
  3. “Great job! I learned a lot. I highly recommend this course to anyone reading this. I also urge those who have taken any form of introductory course in machine learning to do themselves a favor and enroll in this course. It is extremely helpful.” – Adesan Orire Newman


Are you aiming to secure a high-paying job in the field of Data Science?

Are you an experienced AI practitioner looking to advance your Data Science career further?

Or perhaps you’re an aspiring data scientist seeking practical experience in Data Science and Artificial Intelligence?

If you answered yes to any of these questions, then this course is tailored for you!

Data Science is currently one of the most in-demand fields in the tech industry, offering numerous career opportunities. It has found applications in various sectors, including banking, healthcare, airlines, logistics, and technology.

The objective of this course is to equip you with the essential knowledge and skills needed to excel in data science within a practical, enjoyable, and accessible learning environment. By utilizing real-world datasets, you will gain valuable hands-on experience throughout the course.

Task #1: Predict Price of Airlines Industry

  • Develop a Machine Learning model to predict fares of airlines on various routes.
  • Gather relevant data on airlines, routes, and historical fare prices.
  • Preprocess and clean the data, handling missing values and outliers.
  • Conducting exploratory data analysis to extract valuable insights from the data.
  • Apply feature engineering techniques to extract meaningful information.
  • Partitioning the data into training and testing sets.
  • Choose and train a suitable Machine Learning model, such as regression algorithms.
  • Evaluate the model’s performance using appropriate metrics.
  • Fine-tune the model by optimizing hyperparameters.
  • Use the trained model to make fare predictions for new data points.

Objective #2: Predict the strength of a password.

  • Develop a model to predict the strength category of a password (strong, good, or weak).
  • Gather a dataset of passwords with their corresponding strength labels.
  • Preprocess the data by cleaning and encoding the passwords.
  • Perform exploratory data analysis to understand the distribution of password strengths.
  • Divide the data into training and testing sets.
  • Choose and train a classification model, such as logistic regression or a neural network.
  • Evaluate the model’s performance using appropriate metrics like accuracy, precision, and recall.
  • Fine-tune the model by adjusting hyperparameters.
  • Use the trained model to predict the strength category of new passwords.

Task #3: Predict Prices of a Stock

  • Build time series forecasting models for predicting future stock prices.
  • Gather historical stock price data for the desired stock.
  • Preprocess and clean the data, handling missing values and outliers.
  • Visualize the data to understand trends and patterns.
  • Split the data into training and testing sets, considering the temporal nature of the data.
  • Choose and train a suitable time series forecasting model, such as ARIMA or LSTM.
  • Evaluate the model’s performance using appropriate metrics, considering factors like accuracy and Mean Absolute Error (MAE).
  • Fine-tune the model by adjusting hyperparameters and considering different lag values.
  • Use the trained model to forecast future stock prices based on new data points.

These tasks will allow you to apply Machine Learning and time series analysis techniques to solve practical problems in the airline industry, password strength classification, and stock price prediction.

In this course, you will gain firsthand experience of the challenges that data scientists encounter on a daily basis. You will encounter various data issues such as corrupt data, anomalies, and irregularities, giving you a realistic understanding of the field.

Throughout the course, you will embark on a comprehensive Data Science journey. By the end of the course, you will have acquired the following skills and knowledge:

  1. Data Collection and Preparation:
    • Learn how to collect and gather relevant data for analysis.
    • Understand the importance of data cleaning and preprocessing to ensure data quality.
    • Gain hands-on experience in preparing data for analysis.
  2. Data Visualization:
    • Explore basic and advanced techniques for visualizing data effectively.
    • Learn how to create meaningful visualizations that highlight important insights.
    • Develop the skills to communicate data visually to facilitate understanding and decision-making.
  3. Data Modeling:
    • Discover various data modeling techniques used in Data Science.
    • Learn how to select appropriate models for different types of data and problems.
    • Develop the ability to build and train models using popular algorithms.
  4. Curve-Fitting:
    • Understand the concept of curve-fitting and its application in Data Science.
    • Learn how to fit curves to your data to capture underlying patterns and relationships.
    • Gain insights into using curve-fitting to make predictions and draw conclusions.
  5. Presentation of Findings:
    • Learn effective strategies for presenting your data analysis findings.
    • Explore techniques to convey complex information in a clear and concise manner.
    • Develop the skills to deliver compelling presentations that captivate and impress your audience.

By completing this course, you will be equipped with a comprehensive set of skills that will enable you to navigate the entire Data Science process, from data collection and cleaning to modeling, curve-fitting, and presenting your findings with confidence and impact.

Here are the reasons why you should consider taking this course:

  1. Real Data and Real-World Problems: The course focuses on projects that involve real data and real-world problems, providing you with practical experience. There are no toy datasets used, ensuring that you gain relevant skills and knowledge to become a Data Scientist, Data Analyst, or ML Engineer.
  2. Comprehensive Coverage of Data Science: The course covers the entire Data Science pipeline, starting from importing messy data to cleaning, merging, aggregating, and performing Exploratory Data Analysis. It also includes preparation and processing of data for Statistics, Machine Learning, NLP, and Time Series analysis, along with effective data presentation techniques.
  3. Hands-On Practice: The course offers numerous opportunities to practice and code on your own. Learning by doing is emphasized, allowing you to reinforce your understanding of concepts and gain proficiency in coding.
  4. Combination of Coding and Business Skills: In real-world projects, both coding and understanding the business context are crucial. This course stands out by teaching in-depth Python coding alongside big-picture thinking. You will learn how to analyze data, draw meaningful conclusions, and present your findings effectively.

By enrolling in this course, you will gain practical skills, experience real-world data challenges, enhance your coding abilities, and develop a holistic understanding of Data Science, setting you on the path to becoming a successful practitioner in the field.

You can also check this course as well: 2023 Bootcamp: The Ultimate Machine Learning & Data Science Journey

This course is designed for

  • Data Science: If you are intrigued by the field of Data Science and want to learn about its various components, this course is for you. It provides a comprehensive overview of Data Science, including AI, Machine Learning, Natural Language Processing, and Time Series Analysis.
  • AI Practitioners: If you are already working as an AI practitioner and looking to expand your knowledge and skills in Data Science, this course can help you take your career to the next level. It covers practical projects and real-world applications, allowing you to enhance your expertise in the field.
  • Aspiring Data Scientists: If you aspire to become a Data Scientist, this course can be an excellent starting point. It covers the fundamental concepts and techniques used in Data Science, providing you with a strong foundation. You will gain hands-on experience with real data and learn the essential skills needed to tackle data-driven challenges.
  • Individuals Interested in Machine Learning and NLP: If you have a specific interest in Machine Learning and Natural Language Processing, this course can help you gain practical experience in these domains. You will work on projects that involve developing Machine Learning models and applying NLP techniques to analyze and process textual data.
  • Those Interested in Time Series Analysis: If you are fascinated by the analysis and forecasting of time-dependent data, this course covers the essential techniques and models used in Time Series Analysis. You will learn how to analyze and predict future values in time series data, particularly relevant in areas like finance, economics, and demand forecasting.

Overall, if you have a curious mind and an interest in Data Science, AI, Machine Learning, Natural Language Processing, and Time Series Analysis, this course will provide you with valuable knowledge and practical experience to explore these domains further.

Here is the decoded magnet link of the course.

Note: First, you will need to encode this code… Click here to encode your decoded magnet link: Encode Decode data

Magnet link 1

Frequently Asked Questions (FAQs)

Q: What programming language will be used in this course?

A: This course primarily uses Python for coding and implementing the projects. Basic knowledge of programming, particularly in Python, is recommended.

Q: Do I need prior experience in Data Science or Machine Learning to take this course?

A: No, this course is designed to be accessible to anyone with basic programming knowledge. It covers fundamental concepts and provides practical hands-on experience, making it suitable for beginners. However, some familiarity with Python programming will be beneficial.

Q: What career opportunities can this course help me pursue?

A: Completing this course can equip you with valuable skills and knowledge in the field of Data Science, AI, Machine Learning, Natural Language Processing, and Time Series Analysis. These are in-demand skills in various industries, including banking, healthcare, e-commerce, and technology. This course can help you pursue career paths such as Data Scientist, Machine Learning Engineer, Data Analyst, or AI Specialist.

Q: Can I apply the skills learned in this course to my own projects?

A: Absolutely! The skills and knowledge gained from this course can be applied to your own projects and data science endeavors. The projects in the course cover various domains and techniques, such as machine learning, natural language processing, and time series analysis. This will enable you to tackle similar problems and develop your own data science solutions.

Q: How long does it take to complete the course?

A: The course duration may vary depending on your learning pace and the time you allocate for studying. It is a self-paced course, allowing you to learn at your own convenience. It is recommended to dedicate a few hours each week to go through the lectures, complete the projects, and practice the concepts.

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