Data Analysis In-Depth (With Python)

Explore the data science career path from data analysis to machine learning, deep learning, computer vision, and NLP, with practical steps for transitioning and advancing in data roles.

Discover the data science lifecycle from data engineering to deploying machine learning models with Python. Master supervised and unsupervised methods, time series, clustering, and data-driven decision making.

Inspect, cleanse, transform, and model data to derive information and guide decisions in data analytics. Use Python tools like pandas, numpy, and seaborn to visualize patterns and inform outcomes.

Explore scalar, vector, and matrix representations in data analysis, using 2D and higher dimensions, including dot products, matrix multiplication, transposition, and introductory dimensionality reduction with eigenvalues and PCA concepts.

Explore probability fundamentals for data analysis, outlining sample space, events, union, intersection, and complement, and compare independent, dependent, and replacement scenarios with practical examples.

Explore probability concepts from discrete versus continuous probability, discrete random variables, and expected value, and apply binomial distribution with real-world examples like dice, cards, roulette, and project evaluation.

Explore probability fundamentals, distinguishing discrete and continuous variables, learning probability density and cumulative distribution functions, and applying normal and standard distributions with z-scores and statistics.

Learn to read and compute probabilities using the normal distribution, z-scores, and sigma ranges, and understand sampling, central limit theorem, margin of error, and confidence intervals.

Explore statistical hypothesis testing by framing null and alternate hypotheses, distinguishing population and sample, and using alpha, p-values, and type i and ii errors to validate data claims.

Explore statistical hypothesis testing with degree of freedom, population standard deviation, sample size, and p-values, including two-sample t-tests, alpha levels, and practical a/b style comparisons.

Explore why Python dominates data analysis and machine learning, including its libraries, portability, and how to use tools like Google Colab and Jupyter Notebook for scalable analytics.

Explores Python basics for data analysis, including data types, strings, lists, and dictionaries, indexing, slicing, mutability, and common Python built-ins, with practical notebook workflows in Jupyter and Colab.

Practice Python data analysis fundamentals by working with lists, dictionaries, and comprehensions; learn sorting, indexing, and function fundamentals including scope and imports.

Explore Python functions, lambda expressions, higher-order functions, partial applications, and generators, with practical notes on imports, exceptions, and data handling in a data analysis context.

Explore numpy arrays, their dimensionality and axes, and learn vectorized operations for fast numerical processing, contrasting numpy with Python lists for data analysis tasks.

Explore pandas basics for data analysis with Python, including series and data frames, indexing with loc and iloc, multi-indexes, and essential data wrangling, cleaning, and time-series operations.

Explore core pandas workflows for data analysis: create and inspect dataframes, index and select data, apply functions, compute summaries, sort, handle missing values, and create dummies.

Explore pandas dataframes, one-hot encoding with get_dummies, indexing, and data preparation techniques for machine learning, including handling missing data, column operations, and reading csv or excel files.

Learn Pandas for data analysis with Python, covering group by, describe, covariance and correlation, replace and map operations, joins, concatenation, and other data wrangling techniques.

Day 18 covers Seaborn, a Python plotting library built on Matplotlib that simplifies data visualization with distributions, correlations, and plots like box, violin, and heatmaps.

Explore seaborn for exploratory data analysis by creating bar plots, histograms, box plots, and scatter plots with Titanic data, gender, class, and survival insights. Tune visuals with hue and scales.

Explore the foundations of exploratory data analysis using a LendingClub loan dataset to understand data distribution, handle missing values and outliers, and engineer informative features for modeling.

Perform exploratory data analysis on the LendingClub loan dataset in Python, clean data by dropping columns with over 30 percent missing values, and prepare categorical features for seaborn visualizations.

Plan data analyst career by completing notebooks and projects, then pursue machine learning, deep learning, reinforcement learning, computer vision, and natural language processing to strengthen resume and land a job.