Marketing Analyst: Learn Sales Forecasting & Market Analysis

Explore basic Python strings created with single or double quotes, including indexing and slicing. Apply common operations such as concatenation, repetition, and methods like lower, upper, split, and join.

Explore basic string operations in Python, including concatenation with the plus operator, measuring length with the built-in length function, and indexing starting at zero.

Master python string handling by applying methods such as lower, upper, and strip to transform and clean text for effective data analysis.

Master Python string methods, including split and replace, to manipulate text and process strings. Apply these techniques to marketing analytics, sales forecasting, and market analysis.

Learn string formatting in Python, using the format method and f-strings to create dynamic strings with placeholders and embed variables like name and age.

Master python lists and other sequence types by practicing indexing, slicing, for loops, append, insert, del, pop, sort, reverse, and converting lists to tuples or sets.

Explore Python tuples, an immutable alternative to lists, with indexing, slicing, iteration, and conversion to lists or sets for fast dictionary-key data handling.

Learn to create and manipulate Python sets using curly braces or the set function, perform add, remove, union, intersection, difference, and symmetric difference, and observe automatic duplicate removal.

Explore Python dictionaries, an unordered collection of key-value pairs, and perform operations such as accessing, updating, adding, removing, and iterating items with methods like get, items, keys, values, and update.

Explore Python control flow with if else, elif, and else statements to test conditions, use logical operators and, or, not, and apply to voting eligibility and age categories.

Learn how to use for loops in Python to iterate over sequences, print items, and pair lists with zip, using range, enumerate, and iterators for efficient looping.

Explore advanced for loop techniques in Python, including unpacking tuples and dictionaries, custom ranges, break/continue/else, list modification and filtering, list comprehension, enumerate, zip, and itertools product.

Explore advanced Python looping techniques, including for loops, while loops, and recursion, plus list and dictionary comprehensions, with practical examples like squaring numbers and safe termination using break conditions.

Explore advanced Python looping with for and while loops, including break, continue, and looping over lists, dictionaries, and events, plus best practices and performance tips.

Learn best practices for while loops in Python, including pre-setting loop variables, using descriptive names, avoiding infinite loops with break, and choosing when to use while vs for.

Learn how to define and call Python functions using def, with parameters and return values. Explore creating reusable code blocks like greet and add to process inputs and produce outputs.

Apply positional and keyword arguments to Python functions, and use default parameters to provide fallback values, as shown with name and age examples.

Explore recursion in Python, learning how a function calls itself with base and recursive cases to solve problems by breaking them into simpler subproblems, with countdown and factorial examples.

Explore Python function decorators and higher order functions to modify behavior without changing code, and create flexible, reusable functions through practical examples.

Master lambda functions in Python, concise anonymous one-line functions defined without a name. Apply them with higher-order functions like map, filter, and reduce to perform on-the-fly computations.

Explore function caching and memoization to boost performance by storing and reusing results, with Python examples using functools, Fibonacci, and denormalization.

Learn error handling in Python by using try and except blocks to manage errors, with examples like name error and something else went wrong.

Explore Python modules and libraries, import and reuse code, and build simple even-or-odd function. Learn file handling with open, read, and close, and use os to remove files.

Learn to create and manipulate data frames in Python with pandas, read market history data from CSV, clean and analyze it, and visualize stock prices over time with matplotlib.

Learn to access and manipulate data in Python data frames. Use names and indexes, including column selection, iloc, loc, and multi-level indexing, for efficient financial data analysis.

Explore data aggregation and summarization in Python using real-world financial data. Compute mean and median prices, perform group by stock, and visualize distributions with matplotlib.

Learn to extend and manipulate data in Python using pandas by creating new columns, dropping unnecessary ones, and applying conditional and functional transformations for financial analysis.

Explore data in Python with pandas, using head, tail, and describe to summarize numeric and non-numeric data, and visualize distributions with box plots.

Learn to filter stock data in Python with pandas, using boolean indexing for price, date ranges, and multiple conditions to extract targeted insights for financial analysis.

Learn how to visualize financial data in Python using matplotlib, including axis labels, titles, multiple line plots, and scatter plots to turn numbers into insights.

Visualize the distribution of financial data with histograms, compare normal and non-normal data using the Shapiro-wilk test, and enhance clarity with a legend.

Visualize trends in real world financial data by creating a histogram of p e ratios to reveal valuation trends, identify and name an outlier, and explore market dynamics with Python.

Visualize financial data in Python by selecting the right plot type. Use line plots for trends, bar plots for categories, and scatter plots for correlations with matplotlib and seaborn.

Learn to perform time series analysis in finance using Python and pandas, from indexing and resampling to creating your first time series with a date column and visualizing trends.

Explore creating and converting date times in Python to manage time series data in finance. Format date times for clear presentation using strftime.

Master Python's datetime capabilities by accessing attributes such as year, month, day, hour, and minute, comparing datetimes, and creating relative date times for time-series financial data analysis.

Examine time series growth rates and compare stock prices and other asset classes with a benchmark by calculating daily percentage changes, merging data with the benchmark, and plotting performance differences.

Apply up-sampling to raise time series frequency and use interpolation to fill gaps in financial data, then visualize and compare interpolated monthly earnings per share with stock performance.

Explore indexing and resampling time series data with Python, switching between annual and monthly frequencies to compute mean stock prices and visualize trends.

Explore expanding window functions in pandas to analyze long-term trends, compare cumulative sum and difference, and simulate $1,000 investments in Google and Apple to understand cumulative returns.

Analyze stock price series with lags to compare current and previous prices, compute daily changes and five-day multivariate returns, and plot the results in Python.

Explore binary classification with scikit-learn, building and evaluating a logistic regression model on generated data, including train-test split, accuracy, and confusion matrix.

Master the supervised learning workflow from data collection and preparation through feature engineering, model training, and evaluation, demonstrated with iris data in Python using pandas and scikit-learn.

Learn to measure model performance with train-test split and accuracy, understand overfitting and underfitting, and visualize how model complexity affects accuracy on the iris dataset using k nearest neighbors classifier.

Explore regression as a supervised technique for predicting numerical values, focusing on linear regression, feature creation, model fitting with scikit-learn, and visualizing predictions with matplotlib on a hypothetical dataset.

Explore the basics of linear regression, learn to fit and predict with regression models, and evaluate performance using mean squared error and r squared.

Explore cross-validation concepts, implement five-fold cross-validation for r squared in a diabetes dataset with linear regression, and analyze mean and standard deviation of the scores for model evaluation.

Explore regularized regression to improve linear models with ridge and lasso, highlighting feature importance, overfitting prevention, and evaluation with mean squared error using scikit-learn.

Explore the k nearest neighbors classification method in supervised learning, detailing fitting the model, predicting on new data, and evaluating accuracy using the iris dataset.

Learn how to choose a primary evaluation metric for a diabetes prediction classifier and evaluate a random forest classifier using accuracy, precision, recall, F1 score, and ROC AUC.

Explore logistic regression for binary classification and visualize performance with the roc curve. Learn to compute roc auc and evaluate model accuracy using scikit-learn in Python.

Explore hyperparameters and tune models with grid search and randomized search using a random forest on the breast cancer dataset, with cross-validation and accuracy.

Learn data preprocessing and how to create dummy variables for categorical features to prepare data for regression models. Apply linear and logistic regression and assess performance with mean squared error.

Learn to handle missing data with strategies, and build a robust machine learning pipeline with imputation, scaling, and a random forest classifier for adult income prediction.

Explore centering and scaling in data preprocessing and see how these techniques improve regression and classification models using scikit-learn, with practical examples on the Boston housing and breast cancer datasets.

Evaluate multiple models to compare regression and classification performance using visualizations, test-set predictions, and a pipeline for predicting song popularity with scikit-learn.

Refresh your scikit-learn basics and master k-nearest neighbors, model comparison, and overfitting mitigation with practical insights, cross-validation, and visualization on Iris and synthetic datasets.

Apply logistic regression and support vector machines to text classification with scikit-learn, using tf-idf features to perform sentiment analysis on movie reviews, including the IMDb dataset.

Explore how linear classifiers use linear decision boundaries to separate classes, visualize decision boundaries with logistic regression, and understand how these boundaries adapt across simple to complex datasets.

Explore linear classifiers by examining model coefficients and the dot product, visualize the decision boundary, and learn how changing coefficients controls predictions and model behavior.

Explore the loss function, including the zero minus one loss, and how minimizing a loss function guides model evaluation and optimization to improve accuracy.

Examine loss function diagrams to compare logistic and hinge losses for binary classification and implement logistic regression, visualizing decision boundaries and model accuracy.

Explore logistic regression with regularization to reduce overfitting. Apply feature selection to identify key words and uncover the most positive and negative terms in sentiment analysis using scikit-learn.

Explore logistic regression beyond classification by obtaining class probabilities, examine how regularization affects them, and visualize easy and difficult instances to understand model confidence.

Explore multi-class logistic regression, count coefficients, and train a multinomial model with scikit-learn, visualize decision boundaries, and compare one-versus-rest SVM for multi-class classification.

Explore unsupervised learning through clustering, determine the optimal number of clusters with the elbow method on 2D data using k-means, and assess quality with inertia and silhouette analysis.

Learn to determine the optimal number of clusters for grain data using silhouette score and k-means, standardizing features and visualizing clustering to evaluate performance.

Apply feature transformation and scaling to improve clustering with the k-means algorithm, then cluster stocks by price movements to identify which move together.

Delve into hierarchical clustering and learning how many merges reveal the structure, visualizing grain and stock hierarchies with dendrograms and complete-linkage methods.

Explore t-SNE for two-dimensional maps to visualize high-dimensional data, apply to datasets and stock market, and reveal patterns in a 2D space.

Visualize the PCA transformation and explore correlated data to understand how principal components reduce dimensions and preserve essential variance in grain measurements.

Learn dimension reduction with PCA to simplify high dimensional data, from fish measurements to tf-idf word frequencies, and apply the technique to text and Wikipedia clustering.

Learn how non-negative matrix factorization uncovers interpretable topics in documents and parts in images, and compare its interpretability with PCA in dimensionality reduction.

Explore building recommender systems with non-negative matrix factorization, leveraging NMF and cosine similarity to deliver personalized article recommendations and find similar articles.