The Ultimate Data Science Crash Course: Unleash Your Analytical Superpowers!

Are you fascinated by the world of data and eager to learn how to extract valuable insights from it? Look no further! In this comprehensive data science crash course, we will delve into the fascinating realm of data analysis, building a strong foundation in this innovative field.

Why Data Science Matters

Data science is the driving force behind today's digital age. With the exponential growth of data, companies and organizations are constantly seeking professionals who can help them make sense of it all. Data scientists are in high demand, and the field offers incredible opportunities for career advancements and personal growth.

As businesses collect and store massive amounts of data, the need arises to transform it into actionable intelligence. Data science uses various techniques, including statistics, mathematics, and programming, to extract meaningful insights from data sets. These insights drive informed decision-making and enable organizations to gain a competitive edge.

DATA SCIENCE CRASH COURSE: Voice Based Gender Classification and Prediction Using Machine Learning and Deep Learning with Python GUI

by New Directions (Kindle Edition)

4.8 out of 5

Language	:	English
File size	:	18028 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Print length	:	414 pages
Lending	:	Enabled

FREE

The Basics of Data Science

Before diving into the intricacies of data analysis, it is essential to understand the key concepts and tools used in data science:

1. Python Programming

Python is the most popular programming language among data scientists due to its simplicity and extensive scientific libraries. We will provide you with a step-by-step tutorial on Python programming, teaching you the fundamentals and guiding you through practical examples.

2. Statistics and Probability

A solid understanding of statistics and probability theory is crucial when working with data sets. We will demystify these concepts and show you how to apply them to gain insights from your data.

3. Data Wrangling and Cleaning

Data is often messy, inconsistent, and incomplete. In this crash course, you will learn how to preprocess and clean data, ensuring its quality and reliability for analysis.

4. Exploratory Data Analysis

Exploratory data analysis (EDA) is the process of understanding the main characteristics of a dataset. We will guide you through various techniques and visualizations to gain insights, identify patterns, and uncover hidden relationships in your data.

5. Machine Learning

Machine learning is a powerful tool used in data science to make predictions and automate decision-making processes. We will introduce you to the fundamentals of machine learning and guide you through its practical applications, such as classification, regression, and clustering.

Hands-On Learning Experience

This data science crash course emphasizes hands-on learning to ensure you gain practical skills that are directly applicable in real-world scenarios. Throughout the course, you will work on various datasets and projects, allowing you to put into practice the concepts and techniques you have learned.

By working on real data, you will encounter the challenges that data scientists face daily, such as data quality issues, missing values, and noisy data. Overcoming these challenges will sharpen your problem-solving skills and make you a more competent data scientist.

The Future of Data Science

The field of data science is continually evolving, with new techniques and technologies emerging rapidly. By enrolling in this crash course, you will not only learn the current best practices but also gain a solid foundation to adapt and grow with the field.

As data becomes increasingly important in all aspects of life, from healthcare to finance, data scientists will play a critical role in shaping the future. By mastering the skills taught in this crash course, you will be well-equipped to contribute to groundbreaking advancements and make an impact in your chosen field.

Enroll Today and Unleash Your Analytical Superpowers!

Don't miss out on this incredible opportunity to dive into the world of data science and unlock your analytical superpowers. Enroll in our crash course today and equip yourself with the skills and knowledge to become a sought-after data scientist. Be prepared to embark on an exciting journey filled with mind-blowing discoveries and endless possibilities!

DATA SCIENCE CRASH COURSE: Voice Based Gender Classification and Prediction Using Machine Learning and Deep Learning with Python GUI

by New Directions (Kindle Edition)

4.8 out of 5

Language	:	English
File size	:	18028 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Print length	:	414 pages
Lending	:	Enabled

FREE

This dataset was created to identify a voice as male or female, based upon acoustic properties of the voice and speech. The dataset consists of 3,168 recorded voice samples, collected from male and female speakers. The voice samples are pre-processed by acoustic analysis in R using the seewave and tuneR packages, with an analyzed frequency range of 0hz-280hz (human vocal range).

The following acoustic properties of each voice are measured and included within the CSV: meanfreq: mean frequency (in kHz); sd: standard deviation of frequency; median: median frequency (in kHz); Q25: first quantile (in kHz); Q75: third quantile (in kHz); IQR: interquantile range (in kHz); skew: skewness; kurt: kurtosis; sp.ent: spectral entropy; sfm: spectral flatness; mode: mode frequency; centroid: frequency centroid (see specprop); peakf: peak frequency (frequency with highest energy); meanfun: average of fundamental frequency measured across acoustic signal; minfun: minimum fundamental frequency measured across acoustic signal; maxfun: maximum fundamental frequency measured across acoustic signal; meandom: average of dominant frequency measured across acoustic signal; mindom: minimum of dominant frequency measured across acoustic signal; maxdom: maximum of dominant frequency measured across acoustic signal; dfrange: range of dominant frequency measured across acoustic signal; modindx: modulation index. Calculated as the accumulated absolute difference between adjacent measurements of fundamental frequencies divided by the frequency range; and label: male or female.

The models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, XGB classifier, MLP classifier, and CNN 1D. Finally, you will develop a GUI using PyQt5 to plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy.