Crash Course on Data Analytics using Python - IM
₹
750
₹
1,500
50% off
Exclusive of taxes
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:</p><p>Visualize and understand your data through summary statistics, data visualization, and initial insights.</p><p>Tools like “matplotlib”, “sea born”, and pandas for data exploration are commonly used.</p><p>4. Data Visualization:</p><p>Create meaningful visualizations to communicate data insights effectively.</p><p>Use libraries like “matplotlib”, “sea born” and “plotly” for plotting and charting.</p><p>5. Statistical Analysis:</p><p>Conduct statistical tests and hypothesis testing to make inferences about the data.</p><p>Libraries like ”scipy ” and statsmode1s provide statistical functions.</p><p>6. Machine Learning (Optional):</p><p>Apply machine learning algorithms for predictive modelling, classification, and clustering tasks.</p><p>“scikit-learn” is a popular library for machine learning in Python.</p><p>7. Time Series Analysis (Optional):</p><p>For time-series data, use libraries like stats models or specialized packages like prophet for forecasting and analysing trends.</p><p>8. Big Data Processing (Optional):</p><p>For large-scale data analytics, consider tools like Pie Spark for distributed data processing and analysis.</p><p>9. Reporting and Documentation:</p><p>Create reports, presentations, and documentation using tools like Jupiter Notebook, which allows you to combine code, visualizations, and explanations in a single document.</p><p>10. Automation and Workflow Management (Optional):</p><p>Streamline data analytics tasks and automate data pipelines using tools like Apache Airflow.</p><p><br></p><p><br></p><p><span style="color: rgb(0, 0, 0);">Data Analytics using Python </span>course will be added to the LMS Portal, we will send notify to your registered mail. If you didn't receive according login credentials.</p>&redirect_uri=http://www.facebook.com)
Crash Course on Data Analytics using Python - IM
₹
750
₹
1,500
50% off
Exclusive of taxes
Share this
Quantity
1
Product Description
Data analytics using Python involves a variety of techniques and tools for exploring, processing, and deriving insights from data. Here's an overview of the key steps and tools typically involved in data analytics using Python.
1. Data Collection:
Acquire data from various
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