1. Introduction and Objective****
In my data analytics project, I undertook a comprehensive analysis workflow to address the increasing demand for data-driven decision-making in modern organizations. My primary objective was to establish database connectivity and conduct thorough analytical procedures to extract meaningful insights. Through the implementation of machine learning algorithms and advanced visualization techniques, I developed a framework that transforms raw data into actionable intelligence, enabling strategic decision-making processes. My approach focused on delivering tangible value through systematic data exploration and interpretation.
2. **Project Structure**
• In my project workflow, I have implemented several key stages to ensure robust data analysis and insight generation:
• First, I establish a secure connection to our cloud-based SQL Server database through the pyodbc library, implementing environment variables to maintain security protocols. This forms the foundation of my data extraction process.
• Following data acquisition, I conduct thorough data processing and cleaning operations. This critical step allows me to address missing values, identify and handle outliers, and resolve any data inconsistencies, thereby ensuring the integrity of my subsequent analyses.
• In the exploratory data analysis (EDA) phase, I generate initial visualizations and compute statistical summaries to uncover underlying patterns, temporal trends, and significant correlations within my dataset.
• I then advance to sophisticated Machine learning and predictive modeling, where I utilize sklearn and complementary tools to develop models that yield deeper analytical insights. These models enable me to either predict emerging trends or classify data according to project requirements.
• Finally, I create comprehensive visualizations and reports using plotly and matplotlib libraries. This ensures my findings are communicated effectively to stakeholders through clear, interactive visual representations
3. Technical Content
Technologies
Python
Pandas,
jupyter
Power BI
I employed a comprehensive series of technical approaches to execute this project successfully:
In the initial phase, I established a secure connection to SQL Server through a carefully configured connection string, enabling me to extract the necessary raw data. I then proceeded with data wrangling and exploratory data analysis, leveraging pandas and seaborn libraries to construct initial data frames and generate insightful visualizations. To enhance user engagement, I implemented Plotly's interactive charting capabilities, allowing stakeholders to dynamically explore the revealed patterns.
II.For the analytical component, I developed predictive models using sklearn's machine learning algorithms, which enabled me to uncover deeper insights beyond traditional descriptive statistics. My visualization strategy incorporated both static and interactive elements—I created histograms, scatter plots, and heatmaps to illustrate key correlations, while implementing Plotly graphs to facilitate in-depth data exploration. Which can be seen in the following link [https://github.com/ndumbe0/LP1-Project-Sprint/blob/d6cff21a04e15c04e890cf9c4f5364e269c0b976/test%20%20file.ipynb]
III.To ensure broader accessibility and reporting capabilities, I successfully replicated these visualizations in Power BI, providing stakeholders with a familiar and robust business intelligence platform. [https://app.powerbi.com/view?r=eyJrIjoiNDFlYjRkMDQtYTVhOC00Nzc4LWJjNjYtZDU5MGQyYWMxNGQ1IiwidCI6IjQ0ODdiNTJmLWYxMTgtNDgzMC1iNDlkLTNjMjk4Y2I3MTA3NSJ9]
4.Conclusions and Recommendations
Through my analysis, I have uncovered significant findings that can drive strategic improvements in our operations. Specifically:
• Through my exploratory data analysis and modelling work, I identified key trends that can facilitate more targeted decision-making. These insights offer concrete areas for improvement and highlight promising growth opportunities.
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1. Which industries received the highest average funding during 1980-2021?
This bar plot titled 'Average Funding by Industry (1982-2021)' displays the average funding amount for each industry from 1982 to 2021. The plot shows that the top funded industries include 'Gaming', 'Social Media', 'E-commerce', 'Aerospace', and 'Renewable Energy'.
The 'Gaming' industry has the highest average funding, exceeding 1 billion dollars. The 'Social Media' and 'E-commerce' industries also have high average funding, exceeding 600 million dollars. The 'Aerospace' and 'Renewable Energy' industries have an average funding of over 400 million dollars.
Other industries, as shown in the plot, have average funding amounts ranging from around 100 million dollars to 400 million dollars.
2. How does the funding round impact the amount of funding received by start-ups?
This box plot titled 'Funding Amount by Round/Series' displays the distribution of funding amounts for each funding round/series.
The plot shows that the 'Series C' funding round has the highest median funding amount, exceeding 200 million dollars. The 'Series D' and 'Series B' funding rounds also have high median funding amounts, exceeding 100 million dollars.
Other funding rounds, as shown in the plot, have median funding amounts ranging from around 10 million dollars to 100 million dollars. The 'Seed' funding round has the lowest median funding amount, less than 10 million dollars.
This bar plot titled 'Average Funding Amount by Round/Series' displays the average funding amount for each funding round/series.
The plot shows that the 'Series F' funding round has the highest average funding amount, exceeding 400 million dollars. The 'Venture Round' and 'Series E' funding rounds also have high average funding amounts, exceeding 200 million dollars.
Other funding rounds, as shown in the plot, have average funding amounts ranging from around 10 million dollars to 100 million dollars. The 'Seed' funding round has the lowest average funding amount, less than 10 million dollars.
3. How did the funding amounts in 2020 (during the pandemic) compare to the funding amounts in 2018 and 2019? Did the pandemic lead to a decline or an increase in funding?
This bar plot titled 'Total Funding by Year (2018-2020)' displays the total funding amount for each year from 2018 to 2020.
The plot shows that the year 2018 had the highest total funding, exceeding 2.4 billion dollars. The years 2019 and 2020 have total funding amounts of 1.04 billion dollars and 986 million dollars, respectively.
4. Which industries experienced the most significant changes in funding before and during the pandemic? Did certain sectors thrive while others struggled?
This bar plot titled 'Change in Funding by Industry (Pre vs During Pandemic)' displays the change in funding for each industry before and during the pandemic.
The plot shows that the industries with the largest increase in funding during the pandemic include 'Gaming', 'Social Media', 'E-commerce', 'Aerospace', and 'Renewable Energy'.
The 'Gaming' industry has the largest increase in funding, exceeding 400 million dollars. The 'Social Media' and 'E-commerce' industries also have significant increases in funding, exceeding 200 million dollars. The 'Aerospace' and 'Renewable Energy' industries have increases in funding exceeding 100 million dollars.
Other industries, as shown in the plot, have either experienced increases or decreases in funding ranging from less than 100 million dollars to over 100 million dollars.
5. What characteristics ( location) are associated with higher funding amounts?
This bar plot titled 'Total Funding Amount by Location' displays the total funding amount for each location.
The plot shows that Bangalore has the highest total funding amount, exceeding 10 billion dollars.
Other locations, as shown in the plot, have total funding amounts ranging from less than 1 million dollars to over 1 billion dollars. The locations with the lowest total funding amounts include 'Ahmadabad', 'Andheri', and 'Goa'.
This heatmap titled 'Heatmap of Total Funding Amount by Location and Industry' displays the total funding amount for each industry and location.
The plot shows that the industries with the highest total funding amounts include 'Gaming', 'Social Media', 'E-commerce', 'Aerospace', and 'Renewable Energy'.
The 'Gaming' industry has the highest total funding amount, exceeding 1 billion dollars. The 'Social Media' and 'E-commerce' industries also have high total funding amounts, exceeding 600 million dollars. The 'Aerospace' and 'Renewable Energy' industries have total funding amounts exceeding 400 million dollars.
Other industries, as shown in the plot, have total funding amounts ranging from around 100 million dollars to 400 million dollars. The locations with the highest total funding amounts include Bangalore, Mumbai, and Gurugram.
6. How did the total amount of funding for Indian start-ups change from 2010 to 2021? What are the yearly trends? Did Covid 19 have an impact?
This line plot titled 'Total Amount of Funding for Indian Start-ups (1982-2021)' displays the trend of total funding amount by year.
The plot shows that there was a significant increase in total funding amount from 2014 to 2021, with some fluctuations.
Before 2014, the total funding amount remained relatively low, with a few minor peaks.
In 2014, there was a noticeable jump in total funding, followed by a slight dip in 2015 and subsequent fluctuations until 2020.
The year 2021 saw the highest total funding amount, exceeding 4 billion dollars.
•Based on the above results, I strongly recommend enhancing our data collection methods, as higher-quality data will yield improved model accuracy. Furthermore, I suggest expanding our analytical approach to incorporate more sophisticated machine learning techniques, which could uncover additional valuable insights.
My project demonstrates the critical importance of implementing a structured approach to data analytics, encompassing everything from secure data extraction to actionable insights. I conclude that organizations seeking to leverage data for decision-making must prioritize investment in robust analytics workflows and tools.
_Appreciation _
I highly recommend Azubi Africa for their comprehensive and effective programs. Read More articles about Azubi Africa here and take a few minutes to visit this link to learn more about Azubi Africa life-changing programs
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Azubi Data Science
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