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Data Science Programmer
This learning track provides hands-on application of Programming Constructs, Data Structures and prominent Data Science Libraries in Python; Data Handling, Descriptive Analysis, Predictive Modelling and Text Analysis in Python.
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24 Courses
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Difficulty: Intermediate
Foundational knowledge or experience in statistics or analytics is recommended
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Duration: Approximately 6 months
Suggested learning pace is 5hr/week
Course Overview
- Develop a working understanding of Data Management and Data Pre-processing techniques using Python so as to prepare data for further 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, visualization and analysis purposes.
- Learn to implement the different techniques of data pre-processing, descriptive analysis, dimensionality reduction, predictive modelling and text analysis using Python.
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
- Fundamentals of Data Processing in Python
- Data Science with Pandas
- NumPy for Data Science
- Getting started with Matplotlib
- Data Visualization with Seaborn
- Univariate Exploratory Data Analysis using Python
- Multivariate Exploratory Data Analysis using Python Hypothesis Testing in Python
- Feature Engineering in Python Simple Linear Regression using Python
- Multiple Linear Regression using Python
- Logistic Regression in Python
- Support Vector Machines in Python
- Naïve Bayes in Python
- Tree Models in Python
- Boosting Algorithms in Python
- Clustering Algorithms in Python
- PCA in Python
- Artificial Neural Networks in Python
- Convolutional Neural Networks in Python
- Recurrent Neural Networks in Python
- Introduction to Text processing in Python
- ML Models for Text Data in Python
- Sentiment Analysis in Python
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.
Complete courses to earn sharable certificates and badges. These awarded items will be look great in your portfolio as you showcase your skills and project experience to employers and colleagues.
Learner Outcomes
Complete learning tracks to earn sharable certificates and badges. These awarded items will look great in your portfolio as you showcase your skills, projects, and experience to employers and colleagues.
- The learner will be able to understand and apply different tools and techniques for data management, visualization, transformations, treatment, pre-processing, feature engineering and dimensionality reduction using Python.
- The learner will be able to understand, identify and apply a suitable statistical test to verify the various hypotheses formulated for the problem in-hand.
- The learner will be to identify and apply various Machine Learning (Supervised, Unsupervised, Text Analysis) techniques to real world data from different industries to generate business insights.
- The learner will be able to implement different functionalities within the various Python libraries like Pandas, NumPy, Seaborn, Matplotlib etc. for data manipulation, visualization and analysis purposes.
- The learner will be able to 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.
