Python for Data Science - NumPy, Pandas & Scikit-Learn

About Python for Data Science - NumPy, Pandas & Scikit-Learn

The "Python for Data Science - NumPy, Pandas & Scikit-Learn" course is a comprehensive guide to Python's most powerful data science libraries, designed to provide you with the skills necessary to tackle complex data analysis projects.

This course is tailored for beginners who want to delve into the world of data science, as well as experienced programmers who wish to diversify their skill set. You will learn to manipulate, analyze, and visualize data using Python, a leading programming language for data science.

The course begins with an exploration of NumPy, the fundamental package for numerical computing in Python. You'll gain a strong understanding of arrays and array-oriented computing which is crucial for performance-intensive data analysis.

The focus then shifts to Pandas, a library designed for data manipulation and analysis. You'll learn to work with Series and DataFrames, handle missing data, and perform operations like merge, concatenate, and group by.

The final section of the course is dedicated to Scikit-Learn, a library providing efficient tools for machine learning and statistical modeling. Here you'll delve into data preprocessing, model selection, and evaluation, as well as a broad range of algorithms for classification, regression, clustering, and dimensionality reduction.

By the end of the "Python for Data Science - NumPy, Pandas & Scikit-Learn" course, you will have a firm grasp of how to use Python's primary data science libraries to conduct sophisticated data analysis, equipping you with the knowledge to undertake your own data-driven projects.

Data Scientist - Unveiling Insights from Data Universe!

A data scientist is a skilled professional who leverages their expertise in mathematics, statistics, programming, and domain knowledge to extract meaningful insights and valuable knowledge from complex datasets. They utilize various analytical techniques, statistical models, and machine learning algorithms to discover patterns, trends, and correlations within the data.

The role of a data scientist involves tasks such as data collection, data cleaning, exploratory data analysis, feature engineering, and building predictive or prescriptive models. They work closely with stakeholders to understand business needs, formulate data-driven strategies, and communicate findings effectively to support decision-making processes.

Data scientists possess strong analytical and problem-solving skills, as well as a deep understanding of statistical concepts and programming languages such as Python or R. They are proficient in data manipulation, data visualization, and machine learning techniques.

In addition to technical skills, data scientists possess strong communication and storytelling abilities. They can translate complex data findings into actionable insights and effectively communicate them to both technical and non-technical audiences.

Data scientists play a crucial role in various industries, including finance, healthcare, marketing, technology, and more. They help organizations make informed decisions, optimize processes, identify new opportunities, and solve complex problems by harnessing the power of data.

Some topics you will find in the NumPy exercises:

working with numpy arrays

generating numpy arrays

generating numpy arrays with random values

iterating through arrays

dealing with missing values

working with matrices

reading/writing files

joining arrays

reshaping arrays

computing basic array statistics

sorting arrays

filtering arrays

image as an array

linear algebra

matrix multiplication

determinant of the matrix

eigenvalues and eignevectors

inverse matrix

shuffling arrays

working with polynomials

working with dates

working with strings in array

solving systems of equations

Some topics you will find in the Pandas exercises:

working with Series

working with DatetimeIndex

working with DataFrames

reading/writing files

working with different data types in DataFrames

working with indexes

working with missing values

filtering data

sorting data

grouping data

mapping columns

computing correlation

concatenating DataFrames

calculating cumulative statistics

working with duplicate values

preparing data to machine learning models

dummy encoding

working with csv and json filles

merging DataFrames

pivot tables

Topics you will find in the Scikit-Learn exercises:

preparing data to machine learning models

working with missing values, SimpleImputer class

classification, regression, clustering

discretization

feature extraction

PolynomialFeatures class

LabelEncoder class

OneHotEncoder class

StandardScaler class

dummy encoding

splitting data into train and test set

LogisticRegression class

confusion matrix

classification report

LinearRegression class

MAE - Mean Absolute Error

MSE - Mean Squared Error

sigmoid() function

entorpy

accuracy score

DecisionTreeClassifier class

GridSearchCV class

RandomForestClassifier class

CountVectorizer class

TfidfVectorizer class

KMeans class

AgglomerativeClustering class

HierarchicalClustering class

DBSCAN class

dimensionality reduction, PCA analysis

Association Rules

LocalOutlierFactor class

IsolationForest class

KNeighborsClassifier class

MultinomialNB class

GradientBoostingRegressor class