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Data Analyst
This learning track introduces the concepts and techniques of Data Treatment, Aggregations, Transformations, Visualizations, Feature Engineering, Descriptive and Predictive Analysis Techniques with hands-on handling of prominent Data Science Libraries in Python and Data Handling & Descriptive Analysis in Python.
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17 Courses
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9 Projects & Case Studies
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Difficulty: Intermediate
Foundational education or some experience is recommended
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Duration: Approximately 6 months
Suggested learning pace is 5hr/week
Course Overview
- Learn various Data Management and Data Transformation techniques to fetch data from different tables, simple row & column operations and convert them to useable format to improve data inputs to Machine Learning models.
- Learn Data Pre-processing methods for noise removal through outliers and missing value analysis, basics of Data Visualization and other tools & techniques for Univariate & Multivariate data analysis.
- Develop a working understanding of the basics of statistical analysis and hypothesis testing that is used primarily in the domain of finance, marketing, clinical research etc for validating the claims.
- Learn to implement the different functionalities within the various Python libraries used in Data Science like Pandas, NumPy, Seaborn, Matplotlib etc. for data manipulation and analysis purposes.
What’s included
Lifetime Access
Access this learning track for life once completed
Flexible Scheduling
Start learning online immediately, at your own pace
Desktop Only
We recommend completing this learning track on a desktop
Skills you will learn
- Data Science with Pandas
- NumPy for Data Science
- Fundamentals of Data Processing in Python
- Fundamentals of Data Analytics
- Introduction to ANOVA
- Hypothesis Testing in Python
- Fundamentals of Data Pre-processing
- Basic Data Visualization Methods- I
- Data Visualization with Seaborn
- Getting started with Matplotlib
- Univariate Exploratory Data Analysis using Python
- Multivariate Exploratory Data Analysis using Python Data Mining Concepts and Techniques
- Feature Engineering in Python
- Advanced Feature Engineering technique
- Data Transformation
- Data Aggregation
- Analyzing Student Marks – Hypothesis Testing
- EMI Tenure Affinity Testing – A use case for A/B Testing
- Statistical Analysis on Manufacturing Equipment to study the Quality, Strength and Accuracy
- Exploratory Data Analysis on Vehicle Crash data & Fatalities
- COVID-19 Data Exploration & Visualization
- Detecting Credit Card Fraud – Feature Engineering Techniques
- Cleaning & treating – HR attrition case study
- Application of Variable Selection Techniques to Identify the Significant Predictor Variable
How it Works
Learn new skills that will boost your career by enrolling in courses across data analytics, data science, ML and AI. These courses will utilize readings, videos, quizzes, data cases, and even coding exercises to teach you skills and concepts in a way that will solidify your new knowledge for hands-on application.
With our hands-on projects, you will take your newly learned skills along with our 750+ low-code/no-code functions and embedded coding console to complete milestone-based projects. Once completed, you will have effectively applied new skills and concepts to real-world data cases that can be translated directly into your career.
Complete assessments and track your progress in real-time to benchmark your proficiency in relation to key functional areas. As you progress through your courses, our patented platform will utilize ML and AI to record and analyze your inputs and output to provide active feedback and recommendations that will help you learn more effectively than the standard Letter Grade system used today.
Learner Outcomes
Complete learning tracks to earn shareable certificates and badges. These awarded items will look great in your portfolio, resume, and LinkedIn as you showcase your skills and project experience to employers and colleagues.
- Understand and apply different tools and techniques for data management, visualization, transformations, treatment, pre-processing, feature engineering and dimensionality reduction.
- Identify and apply statistical tests to verify the various hypotheses formulated for the problem in-hand.
- Identify and apply various machine learning techniques to real world data from different industries to generate business insights.
- Implement different functionalities within various Python libraries like Pandas, NumPy, Seaborn, Matplotlib etc. for data manipulation and analysis purposes.
- Interpret and visualize the outcome of the various applied techniques.
“Rolai provides contextual upskilling opportunities … on one single platform.”
“An excellent tool for anyone who wants to quickly learn the ropes.”
“Rolai is the best program available for someone looking to enhance their skills”
“Great way to learn data analytics and data science”
“The courses were excellent and covered topics that I didn’t expect”
Rolai’s patented process provides a personalized learning process for each user. Rolai goes deeper than simply learning concepts and testing your skills. At Rolai, learners can apply their skills to actual industry use cases and projects.
Our courses include readings, videos, quizzes, and hands-on data cases that are completed using our virtual lab; give learners an applied learning experience.
No additional tools are needed to begin learning with Rolai. Our virtual lab contains the necessary data workspace and an embedded coding console.
- We have internal SMEs across industries and domains that we work with to develop relevant content and assure quality datasets and problem statements.
- We also work with enterprises and universities to develop new content directed towards their industry and expertise.
