Learn Data Science Tutorial With Python
Data science enables organizations to make informed decisions, solve problems, and understand human behavior. As the volume of data grows, so does the demand for skilled data scientists. The most common languages used for data science are Python and R, with Python being particularly popular as:
- Easy to Learn: Python’s readable syntax makes it accessible to beginners.
- Rich Library Ecosystem: Python provides extensive libraries such as Pandas and NumPy, essential for data analysis and machine learning.
- Strong Community Support: Python boasts a large and active community, offering ongoing support and learning opportunities.
The Data Science with Python tutorial will guide you through the fundamentals of both data science and Python programming.
Before starting the tutorial, you can refer to these articles:
- What is Data Science?
- Why Do We Need Data Science?
- Python for Data Science
- Setting Up a Data Science Environment
Python Libraries for Data Science
- Pandas for Data Manipulation
- NumPy for Numerical Computing
- Scikit-learn for Machine Learning
- Matplotlib for Data Visualization
Data Loading
- Loading a CSV File into a DataFrame using pandas.read_csv()
- Loading Data from an Excel File using pandas.read_excel()
- Loading Data from JSON Files using pandas.read_json()
- Loading Data from SQL Databases using pandas.read_sql()
- Web Scraping using BeautifulSoup to Scrape Data
- Loading Data from MongoDB into a Pandas DataFrame using pymongo
Data Preprocessing Using Python
- What is Data Preprocessing?
- Working with Missing Data using Pandas
- Detecting Duplicate Rows in a DataFrame
- Removing Duplicates using drop_duplicates()
- Scaling and Normalization of Data
- Feature Transformation of Data Columns
- Feature Selection using Sklearn
- Handling Categorical Data using Label Encoding
- Handling Categorical Data using One-Hot Encoding
- Handling Categorical Data using Ordinal Encoding
- Identifying Outliers in Data
- Detecting outlier using Z score
- Detecting outlier using Interquartile Range
- Box-Cox Transformation to Normalize Skewed Data
- Handling Imbalanced Data
- Splitting Data into Training and Test Sets
- Efficient Preprocessing for Large Datasets
Data Analysis
- What is Data Processing?
- Exploratory Data Analysis
- Univariate and Multivariate Analysis
- Using Pandas describe() to Summarize Data
- Identifying Skewness and Kurtosis
- Calculating Correlation using pandas.corr()
- Hypothesis testing using Python
- One-sample t-test using Python
- Two Sample t-test using Python
- ANOVA Analysis using StatsModels
- Aggregating and Grouping Data Using groupby()
- Statistical Tests for Categorical Data: Chi-Square Test
- Applying PCA for Dimensionality Reduction in Python
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Data Visualization
Importance of Data Visualization in Data Science
- Data Visualization using Matplotlib
- Data Visualization using Seaborn
- Using Plotly for Interactive Data Visualization in Python
- Interactive Data Visualization with Bokeh
Data Visualization using Matplotlib
- Line Plot
- Bar Plot
- Histogram
- Box Plot
- Scatter Plot
- Pie Chart
- Stacked Bar Plot
- Step Plot
- Hexbin Plot
- 3D Plot
- Quiver Plot
Data Visualization using Seaborn
- Pair Plot
- Facet Grid
- Count Plot
- Strip Plot
- Swarm Plot
- KDE Plot (Kernel Density Estimate)
- Joint Plot
- Reg Plot (Regression Plot)
- Boxen Plot
Machine Learning
Supervised Learning
1. Regression
- Linear Regression
- Univariate Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- Random Forest Regression
2. Classification Algorithms
- Logistic Regression
- Softmax Regression
- Naive Bayes
- Support Vector Machines(SVMs)
- Decision tree
- Random Forest Classifier
- Voting Classifier
- Bagging classifier
- K Nearest Neighbors
Unsupervised Learning
- K-Means Clustering
- Elbow Method for Optimal K
- K-Means++ Algorithm
- Mini Batch K-Means Clustering
- DBSCAN Algorithm Implementation
- OPTICS Clustering with Sklearn
- Agglomerative and Divisive Clustering
Applications of Data Science
Data science is used in every domain.
- Healthcare : Healthcare industries uses the data science to make instruments to detect and cure disease.
- Image Recognition : The popular application is identifying pattern in images and finds objects in image.
- Internet Search : To show best results for our searched query search engine use data science algorithms. Google deals with more than 20 petabytes of data per day. The reason google is a successful engine because it uses data science.
- Advertising : Data science algorithms are used in digital marketing which includes banners on various websites, billboard, posts etc. those marketing are done by data science. Data science helps to find correct user to show a particular banner or advertisement.
- Logistics : Logistics companies ensure faster delivery of your order so, these companies use the data science to find best route to deliver the order.
Career Opportunities in Data Science
- Data Scientist : The data scientist develops model like econometric and statistical for various problems like projection, classification, clustering, pattern analysis.
- Data Architect : The Data Scientist performs a important role in the improving of innovative strategies to understand the business’s consumer trends and management as well as ways to solve business problems, for instance, the optimization of product fulfilment and entire profit.
- Data Analytics : The data scientist supports the construction of the base of futuristic and various planned and continuing data analytics projects.
- Machine Learning Engineer : They built data funnels and deliver solutions for complex software.
- Data Engineer : Data engineers process the real-time gathered data or stored data and create and maintain data pipelines that create interconnected ecosystem within an company.
FAQs on Data Science Tutorial
What is data science?
Data science is an interconnected field that involves the use of statistical and computational methods to extract insightful information and knowledge from data. Data Science is simply the application of specific principles and analytic techniques to extract information from data used in planning, strategic , decision making, etc.
What’s the difference between Data Science and Data Analytics ?
Data Science Data Analytics Data Science is used in asking problems, modelling algorithms, building statistical models. Data Analytics use data to extract meaningful insights and solves problem. Machine Learning, Java, Hadoop Python, software development etc., are the tools of Data Science. Data analytics tools include data modelling, data mining, database management and data analysis. Data Science discovers new Questions. Use the existing information to reveal the actionable data. This domain uses algorithms and models to extract knowledge from unstructured data. Check data from the given information using a specialised system.
Is Python necessary for Data Science ?
Python is easy to learn and most worldwide used programming language. Simplicity and versatility is the key feature of Python. There is R programming is also present for data science but due to simplicity and versatility of python, recommended language is python for Data Science.
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