Unlock the power of Applied Data Analytics with Python and SQL, gaining hands-on experience in data manipulation, querying, and analysis. This course will teach you how to efficiently use Python for processing and analyzing data, while leveraging SQL to manage and query databases. You'll develop the skills needed to extract meaningful insights and apply data-driven solutions to real-world challenges, preparing you to make informed decisions in any industry.
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Applied Data Analytics with Python and SQL
Ce cours fait partie de Spécialisation Applied Data Analytics
Instructeur : Edureka
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Expérience recommandée
Ce que vous apprendrez
Write Python programs using core concepts like variables, data types, and control flow.
Implement NumPy and Pandas to analyze and process large datasets efficiently and accurately.
Create insightful data visualizations with Matplotlib, Seaborn, and Plotly for effective reporting.
Execute SQL queries to retrieve, manage, and analyze data from relational databases accurately.
Compétences que vous acquerrez
- Catégorie : Python (Programming Language)
- Catégorie : Data Visualization
- Catégorie : Data Manipulation with SQL
- Catégorie : Data Analysis
- Catégorie : Integration of MS SQL with Python
Détails à connaître
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décembre 2024
15 devoirs
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Il y a 4 modules dans ce cours
This module is designed to help learners take a significant step towards launching their careers in data analytics. In the first week, we'll explore how Python programming concepts that are essential for creating efficient programs. Additionally, we'll delve into the role of pre-defined Python features in data analysis. Let's get started!
Inclus
38 vidéos8 lectures5 devoirs3 sujets de discussion
During week two of the course, Learners will be taught how to handle data with NumPy and Pandas in various formats while developing skills to represent data visually using different types of charts and graphics.
Inclus
19 vidéos5 lectures4 devoirs
In this module, learners will explore methods for gathering information from various sources on the internet. They will utilize different Python libraries to process and organize the data for thorough evaluation, as well as connect MS SQL Server with Python.
Inclus
33 vidéos4 lectures5 devoirs
This module is designed to assess an individual on the various concepts and teachings covered in this course. Evaluate your knowledge with a comprehensive graded quiz on Python programming concepts, Data manipulation with NumPy and Pandas with Web-Scraping with Python
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1 vidéo1 lecture1 devoir1 sujet de discussion
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Foire Aux Questions
This course offers a thorough practical application of Python and SQL. Created for Python enthusiasts, it focuses on hands-on skills for manipulating and analyzing data, allowing students to address actual data problems.
Learners will delve into the basics of Python and utilize tools such as NumPy and Pandas for effective data management. They will additionally make use of visualization tools like Matplotlib, Seaborn, and Plotly to present their findings in an efficient manner. Moreover, the course includes instruction on utilizing SQL for data manipulation, empowering students with the skills needed to make informed decisions based on data.
At the conclusion of the course, you will be fully equipped to utilize these skills in different data analysis situations, establishing a solid groundwork for future studies in data analytics.
This course is designed for:
- Freshers looking to enter the fields of data analysis, data science, or artificial intelligence.
- Individuals with a keen interest in programming who want to enhance their technical skills.
- Professionals seeking to upskill in Python and data manipulation with SQL for practical applications in their careers.
- Anyone curious about data and eager to learn how to analyze and visualize it effectively.
Whether you're starting your tech journey or looking to build a strong foundation, this course will guide you through the essentials of Python and SQL.
The course utilizes Google Colab and MS SQL Server as the primary platform for coding operations. Learners may also use integrated development environments (IDEs) like Jupyter Notebook, PyCharm, Spyder, or VS Code for more extensive coding projects if desired.