Data science is a “concept to unify statistics, data analysis, and their related methods” in order to “understand and analyze actual phenomena” with data. It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, domain knowledge, and information science.
What will you learn?
- What is and why Data Science?
- Need for Data Science & Data Scientists in the 21st Century?
- Knowledge of Statistics & Probability
- Convert Real-life problems to Machine Learning Problems
- Programming Hands-on Knowledge on R, Python, SQL Basics
- Data Analysis and Visualization
- Machine Learning
- Text Mining
- Case Studies on Machine Learning and Text Mining to understand and develop a sense of Industry approach
- Neural Networks
- Get a solid understanding and hands-on knowledge of several tools and techniques in Data Science to be industry-ready.
- Will be able to pursue any add-on certifications that need a good understanding of Data Science, to meet the industry’s requirements of having certified professionals.
- Ready to tackle problems from a wide variety of perspectives and perceptions.
Who should go for this training?
The following professionals can go for this course:
- Anyone who is willing to transform their careers for Sexiest Job of 21st Century
What are the pre-requisites for this Course?
- Laptop/Desktop with at least 8 GB of RAM, good capacity Hard Drives and good internet connectivity.
- No programming knowledge needed, foundational knowledge will be provided.
- Discuss what Data Science is, need for Data Science/Scientists
- Discuss Foundations of Data Science, Data Science vs Business Intelligence
- Get familiar with the Terminologies in Data Science such as Data Mining, Machine Learning.
- Understand different types of Analytics in Data Science
- Get a feel of Analytics Project Lifecycle
- Recap of Statistics and Probability basics starting from basic coin toss experiments.
- Get familiar with Probability Terminologies.
- Understand types of probability – Conditional, Marginal and Joint probabilities, Random Variables, Probability Distributions – Normal, Gaussian distributions.
- Statistical Tests – t-test, F-test, Chi-Square test.
- Probability of Success, probability of failure, Odds Ratio, ROC Curve.
- Why learn R?, Intro to RStudio, Installation and Walkthrough
- Importance of R in Statistics and Probability, Operations using R
- R Basics such as loops, data types, etc.,
- Data Import and Manipulation in R
- Exploratory Data Analysis using R
- Data Visualization in R, Reading and Writing data from/to files using R programming.
- What is data visualization?, Understand the Importance of Data Visualization, Good vs Bad Data Visualization
- Get to know the tools and languages for Data Visualization, Best Practices for Data Visualization, Intro to Tableau for Data Science
- Data Analysis using Python, Tableau
- Overview of Machine Learning, types of learning techniques
- Applications of ML, Advantages of moving to ML over traditional processes
- Discussion and hands-on over numerous Machine Learning Techniques:
- Supervised Learning: Linear Regression, Logistic Regression, Naïve Bayes, Decision Trees, Random Forests, K-Nearest Neighbours.
- Unsupervised Learning – K-Means, Clustering
- What algorithm to choose when?
- Log Aggregation – Need
- ELK – Setup
- ELK – FileBeat Setup
- ELK – FileBeat Multiple Log files
- ELK – Filebeat – MultiLine Configuration
- ELK – LogStash – Grok Patterns
- ELK – LogStash – Conditional Grok Patterns
- ELK – LogStash HA Arch – Explore
- ELK – Elastic Search – Query
- ELK – Kibana – Patterns
- ELK – Kibana Dashboards
- ELK – Kibana – Curator Jobs
- PROJECT Setup with ELK
- Understand types of Data – Nominal, Ordinal, Numerical, Categorical data.
- Understand the sources, storage.
- Understand Data Quality, Changes and Data Quality Issues.
- Why Statistics and Probability?
- Measurements of Data, categorization of Data in depth.
- Understand Statistical Terminologies – mean, median, mode, Standard Deviation, Correlation Coefficients, population, sample.
- Different statistical metrics – what to use and when?
- Distance Metrics – Euclidean, Manhattan.
- Standard Deviation, Variance, Bias-Variance Trade-off – Underfitting, Overfitting.
- Outlier Analysis – What and how to spot Outliers, Inter-Quartile Range, Box & Whisker plot, Scatter plot, Cook’s Distance.
- Correlation – Pearson Correlation, Positive & Negative Correlation.
- Confusion Matrix – Precision, Recall, Specificity, F1 Score.
- Regression Error Metrics – MSE, RMSE, MAE, MAPE.
- Python Overview, Python IDEs, Installation of Anaconda and Walkthrough
- Keywords, variables, Built-in functions, intro to Pandas, numpy libraries
- Operations using Python, Python Basics such as loops, data types, etc.,
- Data import using Python, Data Manipulation in Python
- EDA using Python, Data Visualization, Reading and Writing data from/to files using Python programming.
- Intro to SQL, basics of SQL
- Advanced SQL
- Busting common myths about Data Science and Data Science jobs in organizations.
- Introduction and overview of Text Mining
- Different kinds of Analyses – Discussion and Lab: Data Cleaning, Bag of Words, TFIDF, Sentiment Analysis.
- Introduction, Evolution, and Advantages of Neural Networks
- Understand when are neural networks better over ML and vice-versa.
- Understand when and how to use Neural Networks.
- Discussion and hands-on of multiple kinds of Neural Networks: Perceptron, MLP, ANN, CNN, RNN.
- Working with Images using CNN.
Modes of Training
Live interactive sessions delivered in our classroom by our expert trainers with real-time scenarios.
Learn from anywhere over the internet, joining the live sessions delivered by our expert trainers.
Learn through pre-recorded video sessions delivered by experts at your own pace and timing.
Frequently Asked Questions
Our trainer is an OCP & OCM certified consultant and has a significant amount of experience in working with the technology, having 18yrs of experience.
Once you get registered, our back-end team will share you the details to join the session live over an online portal which can be accessed through a browser.
Each of our live sessions is recorded. In case if you miss any, you can request us to share the link to that particular session.
For practical execution, our trainer/technical team will provide server access details to the student
Yes. We do provide the step-by-step document which you can follow and if required our technical team will assist you.
Live-Online training is where you can have a live session with the trainer and clarify queries parallelly.
Pre-recorded sessions are the recorded videos that will be provided to you that you can see, listen, and learn anytime at ur feasible place. For doubts in the videos, you can mail the trainer regarding the same.
You can contact our support team, or just drop an email to firstname.lastname@example.org with your queries.
Visit our website regularly to check discounts offers from time to time. However, we provide a discount for single participants & special discounts for 2 or more participants.
* If the request for cancelation is made within 2 days of enrolment for class, 100% refunded.
* If the request made after 2 days, then Refund is made after deduction of the administration fee.