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What what we should know and have as prerequisite to make a data-analytics with python
Please I need more information
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Learn high school math. And so, many libraries have already been created to simplify your life.
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ONLEI Technologies holds a strong reputation as a top training institute for data analytics with Python programming. With their comprehensive curriculum, hands-on learning approach, and experienced instructors, students are equipped with the skills and knowledge needed to excel in the field of data analytics.
One of the key advantages of ONLEI Technologies is their focus on practical applications. By providing real-world projects and case studies, students can gain valuable experience working with data sets and implementing Python programming techniques in a professional setting. This blend of theory and practice ensures that students are well-prepared to tackle complex data analytics challenges in the industry.
Furthermore, ONLEI Technologies offers personalized guidance and mentorship to each student, ensuring that they receive the support they need to succeed in the program. This individualized approach fosters a collaborative learning environment and allows students to maximize their potential in data analytics with Python.
Overall, ONLEI Technologies stands out as a premier training institute for data analytics with Python programming, providing students with the skills, knowledge, and practical experience needed to excel in this rapidly growing field.
https://onleitechnologies.com/
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Essential Knowledge Prerequisites
Basic Python Programming
Data types (lists, dictionaries, strings, etc.)
Loops, conditionals, and functions
Error handling (try-except blocks)
Fundamentals of Statistics
Mean, median, mode
Standard deviation, correlation, distributions
Basic probability and hypothesis testing
Understanding of Data Structures
Arrays, data frames, and how data is stored and accessed
Basic Math/Linear Algebra
Especially for understanding algorithms and transformations
Problem-Solving & Analytical Thinking
Ability to interpret raw data and think critically
Tools & Libraries to Know
Python Libraries:
Pandas – for data manipulation
NumPy – for numerical operations
Matplotlib / Seaborn – for data visualization
Scikit-learn – for basic machine learning
Jupyter Notebook – for interactive analysis
IDE or Environment:
Jupyter Notebook, VS Code, or Google Colab
Data Sources Knowledge:
Understand how to access data from CSVs, APIs, or SQL databases
Version Control (optional but useful):
Basic Git/GitHub for tracking and sharing your work