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A Data Science with Python training course is a course designed to teach individuals the skills and knowledge needed to use Python for data science tasks such as data analysis, data visualization, machine learning, and more. The course typically covers topics such as the basics of Python programming, the use of libraries such as NumPy, pandas, and scikit-learn, and various data science techniques and algorithms. The course may also include hands-on projects and exercises to help learners apply what they have learned.

The prerequisite for learners is either a high school diploma or an undergraduate degree. It is advised that you start with the courses Introduction to Data Science in Python, Math Refresher, Data Science in Real Life, and Statistics Essentials for Data Science in order to comprehend the Python Data Science course the best possible way. These programmes are provided complimentary with this training.

  • Data analysts or scientists looking to use Python in their work
  • Engineers or developers looking to add data science skills to their toolkit
  • Business professionals or managers looking to gain insights from data
  • Researchers or academics looking to analyze data in their field
  • Anyone who wants to gain a deeper understanding of how to use Python for data science tasks It is beneficial for those who have some prior knowledge in programming and statistics, but not necessarily in python.
Course curriculum
    1. Introduction to Python:
    • Basic data types (numbers, strings, lists, dictionaries, etc.)
    • Variables, expressions, and statements
    • Control flow (if/else, for loops, while loops)
    • Functions and modules
    • Error handling and debugging
    1. Data Analysis with Python:
    • Introduction to NumPy and pandas
    • Data cleaning and pre-processing
    • Data manipulation (slicing, indexing, joining, etc.)
    • Data exploration and summary statistics
    • Working with missing data
    1. Data Visualization with Python:
    • Introduction to matplotlib and seaborn
    • Creating line plots, scatter plots, and histograms
    • Customizing plots (colors, labels, etc.)
    • Creating subplots and multiple plots
    • Creating interactive plots with Plotly and bokeh
    1. Machine Learning with Python:
    • Introduction to scikit-learn
    • Supervised learning (linear regression, logistic regression, k-nearest neighbors, etc.)
    • Unsupervised learning (k-means clustering, hierarchical clustering, etc.)
    • Model evaluation and selection
    • Cross-validation and regularization
    1. Advanced Topics:
    • Natural Language Processing (NLP) using NLTK, spaCy, and Gensim
    • Deep Learning using Tensorflow and Keras
    • Big Data Processing using Dask and PySpark
    • Time Series Analysis
    • Anomaly detection
    • Recommender Systems
    1. Project Work:
    • Selecting a project idea
    • Data collection and cleaning
    • Data analysis and visualization
    • Model building and evaluation
    • Presenting the findings and conclusion
Data Science with Python FAQ’s:
1.What are the prerequisites for the course?

Some courses may require a basic knowledge of programming, while others may not. A course may also require knowledge of statistics or mathematics.

2.Will the course cover both theoretical and practical aspects?

Many Data Science with Python course aim to provide a balance of both theoretical and practical aspects, so that students can learn the concepts and also apply them in practice through hands-on exercises and projects.

3.What kind of projects will be covered in the course?

The projects covered in the course will vary depending on the course provider, but they may include real-world data science projects such as analyzing a dataset, building a predictive model, or creating a data visualization.

4.Will the course provide guidance on how to get a job in Data Science?

Some courses may provide resources and guidance on how to find job opportunities in data science, while others may not.

5.What libraries will be covered in the course?

The specific libraries covered in the course will depend on the course provider, but they may include popular libraries such as NumPy, pandas, matplotlib, seaborn, scikit-learn, TensorFlow, Keras, Pytorch, dask, and others.

6.Will the course be updated for the latest version of python?

Some course providers may update their course content regularly to keep up with the latest version of python and its libraries, while others may not. It is good to check with the course provider or check the curriculum before enrolling in the course.

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