Program Duration
Duration - 56 Hours
Accredited by
NSDC
Fexible Learning
In-center and online
Learn from
Industry Experts
Objective
The objective of data science using Python is to use the Python programming language to analyze and extract insights from large, complex datasets. Python is a popular programming language in the data science community due to its versatility, ease of use, and large ecosystem of data science libraries and tools.
Data manipulation : Using Python libraries such as Pandas and NumPy to import, clean, and transform large datasets.
Data analysis : Using Python libraries such as Matplotlib and Seaborn to create visualizations and explore datasets.
Machine learning : Using Python libraries such as Scikit-Learn and TensorFlow to build and train machine learning models for predictive analytics.
Deep learning : Using Python libraries such as Keras and PyTorch to build and train deep learning models for image and speech recognition, natural language processing, and other advanced applications.
Data visualization : Using Python libraries such as Plotly and Bokeh to create interactive data visualizations and dashboards.
Who Can Learn:
Anyone who has an interest in data analysis and a willingness to learn can learn data science using Python. Data science is a rapidly growing field, and Python is becoming one of the most popular programming languages in the data science community.
Data analysts : Data analysts who want to improve their skills and expand their knowledge of data analysis and data visualization.
Business analysts : Business analysts who want to use data to drive business decisions and improve outcomes.
Programmers : Programmers who want to learn how to apply their programming skills to data analysis and machine learning.
Statisticians : Statisticians who want to use their statistical knowledge to analyze and model large datasets.
Students : Students who are interested in pursuing a career in data science or related fields.
Job Opportunities :
Data science using Python is a highly in-demand field with a wide range of job opportunities. With the increasing amount of data being generated by businesses, there is a growing need for professionals who can analyze and extract insights from large datasets.
Data Analyst : Data analysts use Python to analyze data and extract insights that can help businesses make informed decisions. They are responsible for identifying patterns and trends in data, and presenting their findings to stakeholders.
Data Scientist : Data scientists use Python to build and train machine learning models that can make predictions and identify patterns in data. They are responsible for designing and implementing algorithms and models, and presenting their findings to stakeholders.
Machine Learning Engineer : Machine learning engineers use Python to develop and deploy machine learning models. They are responsible for designing, testing, and deploying models that can make predictions and identify patterns in data.
Data Engineer : Data engineers use Python to design and implement data storage and processing systems. They are responsible for creating databases, designing data pipelines, and ensuring that data is processed efficiently and accurately.
Business Intelligence Analyst : Business intelligence analysts use Python to analyze data and create reports and visualizations that can help businesses make informed decisions. They are responsible for identifying trends and patterns in data, and presenting their findings to stakeholders.
Frequently Asked Questions :
1. What is data science using Python?
Data science using Python is the process of using the Python programming language to analyze and extract insights from large, complex datasets.
2. What are some popular Python libraries used in data science?
Some popular Python libraries used in data science include Pandas, NumPy, Matplotlib, Scikit-Learn, TensorFlow, Keras, and PyTorch.
3. What skills do I need to learn data science using Python?
To learn data science using Python, you should have a strong foundation in programming concepts and some knowledge of statistics and mathematics. It's also important to have critical thinking skills and the ability to communicate complex data insights to non-technical stakeholders.
4. What are some common tasks in data science using Python?
Common tasks in data science using Python include data cleaning, data manipulation, data visualization, statistical analysis, machine learning, and deep learning.
5. What are some job roles in data science using Python?
Job roles in data science using Python include Data Analyst, Data Scientist, Machine Learning Engineer, Data Engineer, and Business Intelligence Analyst.
Course Curriculum
» Introduction to Data Science
» Overview on Python Programming
» Exploratory Data Analysis(EDA)
» Data Visualization
» Data Distribution & Correlation
» Regression Analysis
» Clustering - Hierarchical & K-means
» Classification KNN, Naive Bayes
» Decision Tree, Random Forest
» Text Mining, Word Cloud
» Dimension Reduction, Association Rule mining
» Forecasting/Time Series
API & Libraries
Scikit-learn, Numpy, Pandas, Matplotlib, Xlrd, Random, Time, StatsModel, APYORI