What's Included in Data science Course? #1

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opened 2 months ago by armen2334 · 0 comments
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In a Data Science course in Pune, students typically go through a comprehensive curriculum designed to provide both theoretical knowledge and practical skills. Here's what you can expect:

Introduction to Data Science:

Overview of data science, its applications, and its role in solving real-world problems.
Programming Languages:

Hands-on training in essential programming languages like Python and R, which are widely used in data science for data manipulation, analysis, and visualization.
Data Manipulation and Exploration:

Techniques for collecting, cleaning, and transforming raw data using libraries like Pandas, NumPy, and SQL.
Exploratory Data Analysis (EDA) to understand data patterns, relationships, and anomalies.
Statistics and Probability:

Learning foundational statistics to derive meaningful insights from data.
Topics include descriptive statistics, probability theory, hypothesis testing, and A/B testing.
Data Visualization:

Creating data visualizations using tools like Matplotlib, Seaborn, and Tableau to convey data insights clearly.
Machine Learning:

An introduction to machine learning algorithms such as linear regression, classification, clustering, and decision trees.
Practical implementation of algorithms using libraries like Scikit-learn and TensorFlow.
Big Data and Tools:

Introduction to big data technologies like Hadoop, Spark, and NoSQL databases to handle and analyze large datasets.

In a **[Data Science course in Pune](https://www.sevenmentor.com/data-science-course-in-pune.php)**, students typically go through a comprehensive curriculum designed to provide both theoretical knowledge and practical skills. Here's what you can expect: Introduction to Data Science: Overview of data science, its applications, and its role in solving real-world problems. Programming Languages: Hands-on training in essential programming languages like **[Python](https://www.sevenmentor.com/best-python-classes-in-pune.php)** and R, which are widely used in data science for data manipulation, analysis, and visualization. Data Manipulation and Exploration: Techniques for collecting, cleaning, and transforming raw data using libraries like Pandas, NumPy, and SQL. Exploratory Data Analysis (EDA) to understand data patterns, relationships, and anomalies. Statistics and Probability: Learning foundational statistics to derive meaningful insights from data. Topics include descriptive statistics, probability theory, hypothesis testing, and A/B testing. Data Visualization: Creating data visualizations using tools like Matplotlib, Seaborn, and Tableau to convey data insights clearly. Machine Learning: An introduction to machine learning algorithms such as linear regression, classification, clustering, and decision trees. Practical implementation of algorithms using libraries like Scikit-learn and TensorFlow. Big Data and Tools: Introduction to big data technologies like Hadoop, Spark, and NoSQL databases to handle and analyze large datasets.
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