Essential Math for AI.pdf
18.1 MB
Essential Math for AI
Hala Nelson, 2022
Hala Nelson, 2022
Natural Language Processing with Transformers.pdf
6.4 MB
Natural Language Processing with Transformers
Lewis Tunstall, 2022
Lewis Tunstall, 2022
Cracking Codes with Python.pdf
7.6 MB
Cracking Codes with Python
Al Sweigart, 2018
Al Sweigart, 2018
hands-on-data-science.pdf
15.3 MB
Hands-On Data Science and Python Machine Learning
Frank Kane, 2017
Frank Kane, 2017
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Cloud Computing - A Practical Approach for Learning.pdf
2.4 MB
Cloud Computing
A. Srinivasan, 2014
A. Srinivasan, 2014
Advanced Data Structures and Algorithms.epub
4.8 MB
Advanced Data Structures and Algorithms
Abirami A., 2023
Abirami A., 2023
NumPy_SciPy_Pandas_Quandl_Cheat_Sheet.pdf
134.6 KB
Cheatsheet on Numpy and pandas for easy viewing π
ibm_machine_learning_for_dummies.pdf
1.8 MB
Short Machine Learning guide on industry applications and how itβs used to resolve problems π‘
git-cheat-sheet-education.pdf
97.8 KB
Git commands cheatsheets for anyone working on personal projects on GitHub! πΎ
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ππData Analytics skills and projects to add in a resume to get shortlisted
1. Technical Skills:
Proficiency in data analysis tools (e.g., Python, R, SQL).
Data visualization skills using tools like Tableau or Power BI.
Experience with statistical analysis and modeling techniques.
2. Data Cleaning and Preprocessing:
Showcase skills in cleaning and preprocessing raw data for analysis.
Highlight expertise in handling missing data and outliers effectively.
3. Database Management:
Mention experience with databases (e.g., MySQL, PostgreSQL) for data retrieval and manipulation.
4. Machine Learning:
If applicable, include knowledge of machine learning algorithms and their application in data analytics projects.
5. Data Storytelling:
Emphasize your ability to communicate insights effectively through data storytelling.
6. Big Data Technologies:
If relevant, mention experience with big data technologies such as Hadoop or Spark.
7. Business Acumen:
Showcase an understanding of the business context and how your analytics work contributes to organizational goals.
8. Problem-Solving:
Highlight instances where you solved business problems through data-driven insights.
9. Collaboration and Communication:
Demonstrate your ability to work in a team and communicate complex findings to non-technical stakeholders.
10. Projects:
List specific data analytics projects you've worked on, detailing the problem, methodology, tools used, and the impact on decision-making.
11. Certifications:
Include relevant certifications such as those from platforms like Coursera, edX, or industry-recognized certifications in data analytics.
12. Continuous Learning:
Showcase any ongoing education, workshops, or courses to display your commitment to staying updated in the field.
πΌTailor your resume to the specific job description, emphasizing the skills and experiences that align with the requirements of the position you're applying for.
1. Technical Skills:
Proficiency in data analysis tools (e.g., Python, R, SQL).
Data visualization skills using tools like Tableau or Power BI.
Experience with statistical analysis and modeling techniques.
2. Data Cleaning and Preprocessing:
Showcase skills in cleaning and preprocessing raw data for analysis.
Highlight expertise in handling missing data and outliers effectively.
3. Database Management:
Mention experience with databases (e.g., MySQL, PostgreSQL) for data retrieval and manipulation.
4. Machine Learning:
If applicable, include knowledge of machine learning algorithms and their application in data analytics projects.
5. Data Storytelling:
Emphasize your ability to communicate insights effectively through data storytelling.
6. Big Data Technologies:
If relevant, mention experience with big data technologies such as Hadoop or Spark.
7. Business Acumen:
Showcase an understanding of the business context and how your analytics work contributes to organizational goals.
8. Problem-Solving:
Highlight instances where you solved business problems through data-driven insights.
9. Collaboration and Communication:
Demonstrate your ability to work in a team and communicate complex findings to non-technical stakeholders.
10. Projects:
List specific data analytics projects you've worked on, detailing the problem, methodology, tools used, and the impact on decision-making.
11. Certifications:
Include relevant certifications such as those from platforms like Coursera, edX, or industry-recognized certifications in data analytics.
12. Continuous Learning:
Showcase any ongoing education, workshops, or courses to display your commitment to staying updated in the field.
πΌTailor your resume to the specific job description, emphasizing the skills and experiences that align with the requirements of the position you're applying for.
π4β€1