Data Science 101 Data Analytics Class Python Bootcamp NYC

🎉 **Data Science 101 Data Analytics Class Python Pandas Bootcamp** 📊👩‍💻 --- ### **Course Headline:** _"Python Pandas Bootcamp for Data Analytics - A Non-Programmer's Gateway to Data Science at Wall Street, NYC"_ --- ### **Course Description:** Embark on a journey into the fascinating world of Data Science with our **Data Science 101: Python Pandas Bootcamp Data Analytics Course**. Designed for non-programmers and beginners, this course is tailored to provide you with the foundational knowledge of data analytics using Python's most powerful tool, **Pandas**. Our instructor, Shivgan Joshicourse, brings years of NYC-based teaching experience right to your screen. This isn't just another programming course. It's a carefully crafted introduction aimed at equipping you with the basics of Python analytics and an overview of all essential topics in Data Analytics. Our goal is to kickstart your career in Data Science, providing you with the confidence and skills to tackle real-world data problems. --- ### **What You'll Learn:** - 🔹 **Python for Analytics:** Dive into Python with a focus on Pandas, the go-to library for data manipulation and analysis. - 🔹 **Pandas Objects:** Get familiar with core objects like Series and DataFrames, and understand how they compare to Excel VBA. - 🔹 **Creating DataFrames from Scratch:** Learn how to construct DataFrames using dictionaries or lists, the building blocks of data analysis. - 🔹 **Data Cleaning & Preparation:** Master the art of cleaning and preparing your datasets for analysis by handling missing values and performing data imputation. - 🔹 **Data Manipulation:** Discover how to manipulate DataFrames with Pandas to extract meaningful insights from your data. - 🔹 **Aggregation, Wrangling & Reshaping Data:** Gain proficiency in joining, combining, pivoting, melting, and reshaping datasets. - 🔹 **Time Series Data:** Convert strings to datetime objects with ease, and understand the significance of handling time series data. - 🔸 **Visualizations with Matplotlib:** Learn to visualize your data effectively using Matplotlib, turning numbers into compelling stories. --- ### **Course Highlights:** - **Real-World Experience:** This course is based on the author's own classes taught in NYC, ensuring you receive practical, hands-on knowledge directly from an experienced instructor. - **Foundation for Data Science Careers:** By understanding the basics of Python analytics and data manipulation, you'll be well-positioned to pursue a career in Data Science. - **Engaging Curriculum:** With interactive lessons and real-world examples, the course is designed to make learning engaging and effective. - **Flexible Learning:** Study at your own pace from the comfort of your home or office, with the flexibility to learn when it suits you best. --- ### **Who Should Enroll?** This course is ideal for: - 👩‍💼 Non-programmers looking to enter the field of Data Science. - 🚀 Beginners who have some familiarity with Python and wish to specialize in data analytics. - 📈 Professionals from diverse backgrounds seeking to leverage data for decision-making. - 🎓 Students aspiring to pursue higher education or a career in Data Science. --- ### **Join Our Community of Aspiring Data Scientists Today!** Embark on your Data Science journey with our Python Pandas Bootcamp and transform the way you approach data analytics. Whether you're aiming for Wall Street or the bustling tech hubs, this course will equip you with the skills to make a significant impact in the field of Data Science. 🌟 Enroll now and take your first step towards becoming a data-driven professional!

--- **Overall Course Review** The online course in question has garnered a global rating of 3.05, with recent reviews offering a mixed bag of opinions. The course appears to be suitable for beginners interested in data analysis and Python, but some users have pointed out issues with the teaching method and presentation. Here's a detailed breakdown of the feedback: **Pros:** - **Engagement in Data Analysis**: Many users found the course insightful, particularly for those new to data analysis or who have a keen interest in the field. It has been praised for making data analysis seem less daunting and more accessible. - **Resourcefulness**: Once users became familiar with the course resources, they reported finding it quite easy to follow along. The inclusion of notes before lectures was also highly appreciated as it allowed learners to prepare and understand the material better. - **Detail in Explanation**: Some users commended the detailed steps provided during explanations, which helped them grasp the concepts more clearly. A recommendation for further explanation on code usage situations was made, indicating that while the course was good, there is room for improvement. - **Learning Pace**: The speed of the course was both a positive and negative point. While some found it forced them to think quickly and thus learned more effectively, others felt it was too fast, necessitating frequent pauses to digest the information. **Cons:** - **Tutor's Teaching Method**: Several users criticized the tutor for primarily copying and pasting code without thorough explanation. This approach was felt to be a disservice to learners who wanted a deeper understanding of the material. - **Ambiguity and Speed**: The tutorial was described as not easy to follow, particularly for those without prior knowledge. It was also noted that the pace at which videos were presented made it difficult to keep up, especially with videos that moved quickly and required pausing and replaying frequently. - **Technical Issues**: Technical glitches such as a misaligned mouse cursor, poor audio quality, and outdated examples were mentioned in several reviews. The absence of tangible real-world examples was another point of contention, with some users expressing a preference for actual data sets instead of random numbers. - **Resource Availability**: Some materials within the course were skipped or missing altogether, leading to gaps in the learning experience. This included the lack of certain .csv files needed for practical application. - **Outdated Content**: A few users reported that some links and resources were no longer available and suggested that the content should be updated to reflect current practices and tools. In conclusion, while the course offers a solid foundation in data analysis with Python, there are clear areas where it falls short. The teaching method could significantly benefit from more detailed explanations and fewer copy-paste demonstrations. Addressing the technical issues, updating resources, and providing more complete materials would greatly enhance the learning experience for students. Despite these drawbacks, the course remains a recommended starting point for beginners in data science with Python.