Data Science

Learn how long it takes to learn Python for Data Science in 2021. What does it take to be a Data Scientist in 2021?

Streamlit makes it easy to create interactive dashboards in Python, from installation to deployment. Learn how to use Streamlit's built-in widgets, layout functions, and interactivity to create engaging dashboards. #Streamlit #Dashboard #Python

Discover how to avoid overfitting in machine learning using techniques such as cross-validation, regularization, early stopping, ensemble methods and simplifying the model. #Overfitting #MachineLearning

Learn how to implement a simple trend-following algorithm in Python using the Pandas library, and how to use it to make buy/sell decisions in financial markets. #TrendFollowing #Python #Finance

Discover various methods to analyze the correlation between two variables in Python using functions such as numpy.corrcoef(), pandas.DataFrame.corr(), scipy.stats.pearsonr(), scipy.stats.spearmanr() and seaborn.pairplot() to understand the relationship between the variables.

Learn various methods to perform linear regression in Python using functions such as scipy.stats.linregress(), numpy.polyfit(), scikit-learn and statsmodels to model the relationship between a scalar dependent variable and one or more explanatory variables.

Learn how to perform various statistics on a Pandas DataFrame such as mean, median, mode, standard deviation, variance, minimum and maximum, sum, count and more using built-in methods like .mean(), .median(), .std(), .min(), .max(), .sum(), .count(), .describe() etc.

Learn how to easily read and manipulate data from excel, csv and json formats in Python using popular libraries such as pandas, openpyxl, and json

Learn how to use Python for AI development using popular libraries such as TensorFlow, Keras, PyTorch, scikit-learn, NLTK, spaCy, gensim, OpenCV, pandas, NumPy, Matplotlib and Seaborn.