Data Science, Data Analysis, Machine Learning, Statistical Analysis, Time Series Analysis, Python, Pandas, NumPy, Matplotlib, Seaborn, Project-based Learning, Webinar, Master Class, Digital Marketing, Statistics, Regression, Classification, Clustering, Time Series Forecasting
Join our 21-Day Challenge on Data Science Master Class to enhance your skills in data analysis, machine learning, and statistical analysis. Learn essential tools like Python, Pandas, NumPy, Matplotlib, and Seaborn through hands-on projects and expert-led sessions. Perfect for beginners and professionals alike, this webinar series offers practical insights and real-world applications in the field of data science. Don’t miss this opportunity to level up your data science skills!
Registration form for 21-DAY CHALLENGE ON DATA SCIENCE, UPDATE 2024 MASTER CLASS
The objective of this webinar series is to facilitate the participants’ cognizance of the concepts dealt with for substantial utilization of the same in studying, teaching, research work, and upgrading.
Webinar Details:
- Date: May 8, 2024–May 28, 2024
- Platform: YouTube Live
- Time: 7:00 PM–7:45 PM IST
Day 1: Introduction and Data Collection
Day 1 will give a basic diagram of information science concepts and strategies. Members will learn approximately the significance of information collection, counting different strategies and sources of information procurement. This session will lay the foundation for understanding the role of information in decision-making forms and its centrality in different businesses and applications.
Day 2: Pandas Library
Day 2 will center on the Pandas library in Python, an effective apparatus for information control and investigation. Members will learn how to utilize Pandas to productively handle organized information, including counting, importing and exporting information, information cleaning, sifting, gathering, and amassing.
Day 3: Numpy and Data Concepts
Day 3 will cover Numpy, a crucial bundle for numerical computing in Python. Members will learn about multi-dimensional clusters, cluster control, numerical capacities, and straight variable-based math operations. Furthermore, fundamental information concepts such as information sorts, clusters, and information structures will be investigated.
Day 4: Matplotlib, Seaborn
Day 4 will dive into information visualization utilizing Matplotlib and Seaborn libraries in Python. Members will learn how to make different sorts of plots, including counting line plots, scramble plots, bar plots, histograms, and heat maps. Accentuation will be put on choosing the suitable visualization methods to successfully communicate bits of knowledge from information.
Day 5: Project 1: Exploratory Data Analysis of the Sales Dataset
Day 5 will include a hands-on extension where members will perform an exploratory information examination (EDA) on a deal dataset. Through this, members will learn how to evaluate the dispersion, designs, and connections inside the information, as well as recognize potential bits of knowledge and patterns.
Day 6: Project 2: World Population Data Analysis
Day 6 will highlight a venture centred on analyzing world population information. Members will investigate statistical patterns, population dispersion, and components affecting population development or decrease using information visualization and factual examination methods.
Day 7: Project 3: Powerbi Project Population dataset
Day 7 will introduce members to Control BI, a trade analytics device for visualizing and sharing experiences from data. Participants will learn how to make intelligent dashboards and reports utilizing Control BI, with a particular focus on analyzing population datasets.
Day 8: Statistics 1: Sampling, Randomization, Frequency histogram and distribution, time series, bar and pie graphs, variance, and Correlation
Day 8 covers basic statistical concepts essential to data analysis. To do. Topics include sampling techniques, randomization, frequency histograms and distributions, time series analysis, and visualization methods such as bar graphs and pie charts. Additionally, participants will learn about variance, standard deviation, and correlation measures.
Day 9: Statistics 2: Frequency Table and Stem and leaf, central tendency, variation measure, percentile, box-whisper plot, scatter diagram
Day 9 delves deeper into statistical analysis techniques. Students will learn how to summarize and visualize data distributions by creating frequency tables, stem-and-leaf plots, and boxplots. Central tendency measures, variation measures, percentiles, and scatter plots are also covered.
Day 10: Statistics 3: Linear Correlation, Normal distribution, empirical rule, z-score and probabilities, central limit theorem
Day 10 focuses on advanced statistical concepts and their applications. Participants will learn about linear correlation analysis, normal distributions, rules of thumb, Z-scores and probability, and the central limit theorem. These concepts are critical to understanding the distribution of data and drawing statistical conclusions.
Day 11: Machine learning for data science preprocessing (scaling, encoding)
Day 11 introduces participants to machine learning preprocessing techniques.Topics include feature scaling to standardize data ranges, encoding categorical variables for numerical analysis, handling missing or null values, and more.These preprocessing steps are essential to preparing data for machine learning algorithms.
Day 12: ML outlier detection and handling, null handling
Day 12 focuses on outlier detection and handling techniques in machine learning. Participants will learn how to identify outliers in a dataset and implement strategies to effectively deal with them. It also describes how to handle null or missing values to ensure data integrity.
Day 13: ML (feature selection, feature extraction, train_test_split)
Day 13 covers feature selection and extraction methods in machine learning. Participants will learn how to identify relevant features for predictive modelling and extract meaningful information from raw data. Additionally, the concept of train and test splits for model evaluation is presented.
Day 14: ML Algorithms
Participants will learn about supervised and unsupervised learning algorithms such as regression, classification, clustering, and dimensionality reduction techniques.We will discuss real-world examples and usage examples.
Day 15: ML Hyperparameter Tuning and Evaluation
Day 15 focuses on hyperparameter tuning and model evaluation in machine learning. Participants will learn how to optimize model performance by tuning hyperparameters using techniques such as grid search and random search. We also discuss how to evaluate model performance, including cross-validation and metrics such as precision, recall, and F1 score.
Day 16: Project 4: Customer churn prediction using data science
Day 16 includes a hands-on project where participants use data science techniques to build a customer churn prediction model. It will be. Participants will learn how to preprocess data, select relevant features, train machine learning models, evaluate model performance, and predict customer churn from a business perspective.
Day 17: Project 5: Supply Chain Optimization for a FMCG company using DS
Day 17 includes a project focused on optimizing the supply chain for an FMCG (fast-moving consumer goods) company using data science techniques. Participants will explore strategies for inventory management, demand forecasting, and logistics optimization to improve supply chain efficiency and reduce costs.
Day 18: Project 6: Music Dataset Clustering
Participants will learn how to apply unsupervised learning techniques such as K-means clustering and hierarchical clustering to group similar music tracks based on characteristics such as genre, tempo, and mood.
Day 19: Time series introduction
Participants will learn how to analyze time-dependent data, identify patterns and trends, and create forecasts using time–series models. Topics include time series decomposition, trend analysis, seasonality, and forecasting methods.
Day 20: Project 7: CO2 Emission Prediction Using Time Series prediction
Day 20 will include a project focused on forecasting CO2 emissions using time–series forecasting techniques. Participants will learn how to preprocess time series data, build forecasting models using algorithms such as ARIMA (Auto Regression Integrated Moving Average), and evaluate the model’s performance to forecast future CO2 emissions.
Day 21: Project 8: Time Series Analysis on Microsoft stock data
Day 21 will include a project focused on the time series analysis of Microsoft Stock Data. Participants will learn how to analyze historical stock prices, identify patterns and trends, and make predictions using time–series forecasting techniques. Describes the practical application of time series analysis in financial markets
FAQ
What is the duration of the master class?
The master class runs for 21 days, from May 8, 2024, to May 28, 2024.
Where will the master class sessions be held?
The sessions will be conducted live on YouTube.
What time are the sessions scheduled for?
Each session will be held from 7:00 PM to 7:45 PM IST.
Who can participate in the master class?
The master class is open to anyone interested in data science, including beginners, students, professionals, and enthusiasts.
What topics will be covered in the master class?
The master class covers a wide range of topics including data collection, Pandas library, Numpy, Matplotlib, Seaborn, statistics, machine learning, time series analysis, and various projects related to data science.
Do I need any prior knowledge or experience in data science to participate?
No prior knowledge or experience in data science is required. The master class is designed to cater to participants of all levels, including beginners.
Will the sessions be recorded for later viewing?
Yes, the sessions will be recorded and made available for viewing later on YouTube.
How can I register for the master class?
To register for the master class, please fill out the registration form provided on the website or promotional materials.
Is there any cost associated with participating in the master class?
The cost of participating in the master class is not disclosed. Please refer to the registration form or contact the organizers for more information.
Will there be any assignments or assessments during the master class?
While specific details about assignments or assessments are not provided, participants may be encouraged to complete hands-on projects as part of the learning experience.
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