Data Science in Python: From Preprocessing to Forecasting

From preprocessing intricacies to advanced forecasting techniques. Equip yourself with the skills to navigate the data
Free tutorial
Rating: 4.8 out of 5 (13 ratings)
3,410 students
1hr 54min of on-demand video
English [Auto]

Data Preprocessing: Master techniques for effective data cleaning, formatting, and organization. Learn both basic data cleaning methods and advanced preproces
Data Preprocessing and Feature Engineering: Build on the fundamentals of data preprocessing.
Grasp the concept of feature engineering and its significance in enhancing model performance.
Gain practical insights and make informed decisions during the feature engineering process.
Graph Visualization - Components: Transition into the realm of data visualization.
Explore the components of graph visualization and their importance in representing and interpreting data.
Learn advanced features and customization options for creating compelling visualizations.
Training Model: Shift to practical aspects of model training. Understand modeling and evaluation fundamentals.
Validation and Forecasting: Acquire skills in model validation techniques. Explore forecasting methods to predict future trends based on historical data.
Producing and Visualizing Forecasts: Apply forecasting models to produce and visualize predictions. Learn visualization techniques to enhance interpretability
Comparing Models: Master the critical task of model comparison.
Explore strategies for evaluating and selecting the most effective model for specific use cases.
Installation of Library Prophet: Engage in practical, hands-on activities.
Creating a Model using Prophet: Immerse in the practical process of creating models using the Prophet library.
Evaluation of Model of Prophet Library: Focus on evaluating models created with the Prophet library.


  • Basic knowledge in statistics, mathematics, programming


Welcome to the dynamic realm of "Data Science in Python: From Preprocessing to Forecasting." This comprehensive course is designed to immerse you in the multifaceted world of data science, offering a step-by-step journey from the foundational principles of data preprocessing to the advanced techniques of forecasting using the Prophet library. Whether you're a novice aspiring to delve into the intricacies of data analysis or an experienced professional seeking to refine your skills, this course caters to a broad spectrum of learners.

Embark on a learning adventure where theoretical knowledge seamlessly merges with practical applications. Uncover the nuances of data preprocessing, navigate the complexities of feature engineering, and harness the power of graph visualization components. Transition into the practical aspects of model training, validation, and forecasting, culminating in the utilization of the Prophet library for creating and evaluating predictive models.

Throughout this course, you'll engage in hands-on activities, real-world case studies, and comprehensive exercises, ensuring a holistic understanding of each concept. The curriculum is crafted to empower you with the skills and insights required to tackle diverse data science challenges, from enhancing data quality to making accurate predictions about future trends.

Join us on this transformative learning journey, where you'll not only grasp the theoretical underpinnings of data science but also gain the practical expertise needed to excel in the field. Whether you aim to propel your career forward or simply satisfy your curiosity about the fascinating world of data, this course is your gateway to mastering the intricacies of data science. Let's embark on this educational odyssey together!


This opening section serves as a course prelude, introducing learners to the overarching project's goals and context. It provides clarity on the themes and outcomes expected throughout the course, setting the tone for a comprehensive exploration of the subject matter.

Data Preprocessing:

In this foundational section, learners dive into the intricacies of data preprocessing. The emphasis is on mastering techniques to clean, format, and organize raw data effectively. From basic data cleaning to advanced preprocessing methods, learners gain a solid understanding of the crucial steps preceding data analysis.

Data Preprocessing and Feature Engineering:

Building upon the fundamentals of data preprocessing, this section introduces the concept of feature engineering. Learners explore how to enhance model performance by strategically creating new features. Practical insights into various methods and considerations help learners make informed decisions during the feature engineering process.

Graph Visualization - Components:

Transitioning into visualization, this section explores the components of graph visualization. Learners gain insights into the significance of these components in representing and interpreting data graphically. Advanced features and customization options are covered, empowering learners to create compelling visualizations.

Training Model:

This section marks a shift towards the practical aspects of model training. It covers modeling and evaluation, guiding learners through the fundamentals of creating models and assessing their performance against various metrics. The focus is on building a strong foundation for subsequent sections.

Validation and Forecasting:

Focusing on model validation techniques and forecasting methods, this section equips learners with the skills to validate models effectively. It explores forecasting as a means to predict future trends based on historical data, laying the groundwork for subsequent forecasting-related sections.

Producing and Visualizing Forecasts:

Building upon forecasting, this section emphasizes the application of forecasting models to produce and visualize predictions. Learners explore visualization techniques that enhance the interpretability of forecasts, aiding effective communication of insights to diverse audiences.

Comparing Models:

This section guides learners in the critical task of model comparison. Strategies for evaluating and selecting the most effective model for specific use cases are explored. Comparative analysis adds a layer of sophistication to the model selection process, ensuring informed decision-making.

Installation of Library Prophet:

A practical hands-on section, it guides learners through the installation of the Prophet library. This step is crucial for the subsequent sections where learners will utilize Prophet for creating predictive models in the context of time-series forecasting.

Creating a Model using Prophet:

Continuing from the installation, this section immerses learners in the practical process of creating models using the Prophet library. Hands-on exercises allow learners to apply theoretical knowledge, refining their skills in time-series forecasting using this powerful tool.

Evaluation of Model of Prophet Library:

The final section focuses on the evaluation of models created using the Prophet library. Learners gain insights into assessing model performance, ensuring accuracy, and validating the effectiveness of their time-series forecasting predictions.

Who this course is for:

  • Beginners and Novices: Individuals with little to no prior experience in data science or Python programming. Those looking to explore the field of data science and gain foundational knowledge. Students or recent graduates seeking to enter the data science job market.
  • Intermediate Data Analysts and Scientists: Professionals already working in data-related roles who want to strengthen their skills in Python. Analysts looking to enhance their data preprocessing and feature engineering techniques. Those interested in expanding their knowledge to include time-series forecasting.
  • Experienced Professionals: Data scientists, analysts, or researchers with experience in other programming languages. Professionals aiming to transition to Python for data science tasks. Individuals seeking to refine their expertise in advanced data science concepts.
  • Business and Decision Makers: Managers, executives, and decision-makers who want a comprehensive understanding of data science. Professionals who need to interpret and communicate insights derived from data to support strategic decision-making.
  • Continuous Learners: Individuals committed to continuous learning and staying updated on the latest trends in data science. Lifelong learners interested in expanding their skill set and staying competitive in the job market.
  • Entrepreneurs and Startups: Business owners, entrepreneurs, or startup founders looking to leverage data for business insights. Individuals seeking to understand how data science can be applied to their specific industry or business.


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