Lila's Journey to Becoming a Data Scientist: Her Working Approach on the First Task

 Education and Skill Acquisition:

Lila, with an economics background, decides to transition to data science.

Enrolls in the IBM Data Science Professional Certificate for comprehensive training.

Gains proficiency in statistics, machine learning, and programming languages.

Building a Strong Foundation:

Develops a deep understanding of data manipulation and visualization using Python libraries.

Visualization for Storytelling:

Learns to create informative visualizations, aiding in effective communication of findings.

Hands-On Experience:

Engages in Kaggle competitions and personal data projects for practical experience.

Establishes a GitHub profile to showcase her projects and skills.

Data Wrangling and Preprocessing:

Focuses on data cleaning, preprocessing, and handling missing data and outliers.

Communication and Storytelling:

Hones data storytelling skills using tools like Matplotlib and Plotly.

Networking and Collaboration:

Actively participates in data science communities, attends meetups, and collaborates on open-source projects.

Attends conferences like the IBM TechXchange Conference to gain exposure.

Domain Expertise:

Chooses e-commerce as a niche aligned with her economics background.

Landing the First Job:

Tailors resume and builds an online portfolio to highlight skills and projects.

Lila's Approach to First Task as a Data Scientist:

Employed at a retail company, tasked with improving customer service through data insights.

Dataset Selection and Sourcing:

Faces the challenge of selecting and harmonizing datasets from diverse sources.

Data Understanding and Cleaning:

Imports and examines the dataset, addressing missing values, duplicates, and outliers.

Exploratory Data Analysis (EDA):

Utilizes EDA techniques to gain insights into customer behavior, product popularity, and sales trends.

Feature Engineering:

Explores feature engineering to enhance the dataset's utility for the project.

Statistical Analysis, Machine Learning:

Evaluates the need for statistical tests and machine learning algorithms.

Applies regression analysis and explores machine learning models for demand forecasting.

Presentation and Reporting:

Compiles findings into a comprehensive report and presentation using Jupyter Notebook.

Highlights actionable insights and recommendations for stakeholders.

Continuous Learning:

After the first project, Lila continues refining skills, exploring complex datasets, and taking on challenging tasks.

Machine Learning Skills:

Plans to enhance machine learning skills through the IBM Machine Learning Professional Certificate.

Explores various machine learning algorithms, including linear regression, decision trees, and deep learning models.

Conclusion:

Lila's journey showcases the multifaceted approach to becoming a data scientist, emphasizing hands-on experience, continuous learning, effective communication, and domain expertise. The case study provides insights into the challenges and decision points faced by a data scientist in the real-world scenario of improving customer service in a retail setting.