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

Description

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

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