Data Science Training

Master Data Science:
Learn the skills needed to solve complex data problems

  • 10 - 20 weeks

  • 102 Lectures

  • 502 Student Enrolled
4.5 3572 Reviews


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Data Science Training

DataScience Training

Course Overview

A comprehensive up-to-date Data Science course that includes all the essential topics of the Data Science domain, presented in a well-thought-out structure.

Taught and developed by experienced and certified data professionals, the course goes right from collecting raw digital data to presenting it visually. Suitable for those with computer backgrounds, analytic mindset, and coding knowledge.

What you'll learn

  • Grasp the key fundamentals of data science, coding, and machine learning. Develop mastery over essential analytic tools like R, Python, SQL, and more.
  • Comprehend the crucial steps required to solve real-world data problems and get familiar with the methodology to think and work like a Data Scientist.
  • Learn to collect, clean, and analyze big data with R. Understand how to employ appropriate modeling and methods of analytics to extract meaningful data for decision making.
  • Implement clustering methodology, an unsupervised learning method, and a deep neural network (a supervised learning method).
  • Build a data analysis pipeline, from collection to analysis to presenting data visually.

The world has witnessed explosive digital growth in the last two decades, which has led to a data deluge. This data may be holding some key business insights or solutions to crucial problems. Data Science is the key that unlocks this possibility to extract vital insights from the raw digital data. These findings can then be visualized, and communicated to the decision-makers to be acted upon.

Data Science is an interdisciplinary field requiring statistics, data analysis, programming, and business knowledge.

Listed below are some of the tasks of a typical data scientist.

  • Ask the right set of questions to identify the data-based problems that hold the greatest opportunity for the business.
  • Collect large sets of relevant structured and unstructured data from diverse channels.
  • Process and clean the data to ensure its accurate, complete, and uniform.
  • Choose and apply appropriate data science models and algorithms to mine the big data stores.
  • Perform analysis to identify patterns, trends, and relationships within data. Look for fitting solutions and opportunities.
  • Convert data-based insights into compelling visualizations and present that to stakeholders. Make adjustments to the approach based on the received feedback.

To be able to look at various pieces of data and draw out conclusions is the most valuable skill you can have, a skill that's often missing even amongst technically advanced employees.

Hailed as the "sexiest job of the 21st Century" (Harvard Business Review), here are a few solid reasons to learn Data Science.

  • Expand your problem-solving skills, a skill that's not useful for the professional world, but also in everyday life as well.
  • Data Science is a lucrative career option with an abundance of high paying job opportunities ($113k/yr base pay in the USA (Glassdoor), Rupees 8.15 lakhs in India (PayScale))
  • Generate side income with your data science skill set (Freelance, Start an informative blog/YouTube channel, sell a data science course, or create something innovative with your data knowledge)
  • Get to make the world a better place with data science solutions

No matter what your background is, you can take this data science course provided you're passionate about numbers, and love challenging problems.

But your journey to becoming a successful data scientist would be much easier if:

- You have a background in analytical disciplines such as mathematics, physics, computer science, or engineering.

- You love coding and have a basic understanding of programming languages.

- You are patient enough to keep working on the project even when it seems to have hit a roadblock.

- Most comprehensive and well-structured course covering basics to advanced topics, allowing you to master the complete niche.

- Certified Trainers with extensive real-time experience in the Data Science domain and an immense passion for teaching.

- Top-notch course with a perfect blend of theory, case studies, and capstone projects, along with an assignment for every taught concept.

- 100% Job Placement assistance. Frequent mock interviews to evaluate and improve your knowledge and expertise. Facilitation of interviews with various top companies. Help in building a great resume, optimizing LinkedIn profile, and improving your marketability.

Listed below are some of the leading data science careers you can break into after completing the data science course.

  • Data Scientist
  • Data Analyst
  • Data Engineer
  • Business Intelligence Analyst
  • Marketing Analyst
  • Statistician
  • Database administrator
  • Database developer
  • Data Architect
  • Application Architect
  • Enterprise Architect
  • Infrastructure Architect
  • Machine Learning Engineer
  • Machine Learning Scientist

Course Circullum

  • Recap of Demo
  • Introduction to Types of Analytics
  • Project life cycle
  • An introduction to our E learning platform
  • Topics
  • Data Types
  • Measure Of central tendency
  • Measures of Dispersion
  • Graphical Techniques
  • Skewness & Kurtosis
  • Box Plot
  • R
  • R Studio
  • Descriptive Stats in R
  • Python (Installation and basic commands) and Libraries
  • Jupyter note book
  • Set up Github
  • Descriptive Stats in Python
  • Pandas and Matplotlib / Seaborn
  • Topics
  • Random Variable
  • Probability
  • Probility Distribution
  • Normal Distribution
  • SND
  • Expected Value
  • Sampling Funnel
  • Sampling Variation
  • CLT
  • Confidence interval
  • Assignments Session-1 (1 hr)
  • Introduction to Hypothesis Testing
  • Hypothesis Testing with examples
  • 2 proportion test
  • 2 sample t test
  • Anova and Chisquare case studies
  • Visualization
  • Data Cleaning
  • Imputation Techniques
  • Scatter Plot
  • Correlation analysis
  • Transformations
  • Normalization and Standardization
  • Topics
  • Principles of Regression
  • Introduction to Simple Linear Regression
  • Multiple Linear Regression
  • Topics
  • Multiple Logistic Regression
  • Confusion matrix
  • False Positive, False Negative
  • True Positive, True Negative
  • Sensitivity, Recall, Specificity, F1 score
  • Receiver operating characteristics curve (ROC curve)
  • Topics
  • R shiny
  • Streamlit
  • Topics
  • Supervised vs Unsupervised learning
  • Data Mining Process
  • Hierarchical Clustering / Agglomerative Clustering
  • Measure of distance
  • Numeric - Euclidean, Manhattan, Mahalanobis
  • Categorical - Binary Euclidean, Simple Matching Coefficient, Jaquard’s Coefficient
  • Mixed - Gower’s General Dissimilarity Coefficient
  • Types of Linkages
  • Single Linkage / Nearest Neighbour
  • Complete Linkage / Farthest Neighbour
  • Average Linkage
  • Centroid Linkage
  • Visualization of clustering algorithm using Dendrogram
  • Topics
  • PCA and tSNE
  • Why dimension reduction
  • Advantages of PCA
  • Calculation of PCA weights
  • 2D Visualization using Principal components
  • Basics of Matrix algebra
  • Topics
  • What is Market Basket / Affinity Analysis
  • Measure of association
  • Support
  • Confidence
  • Lift Ratio
  • Apriori Algorithm
  • User-based collaborative filtering
  • Measure of distance / similarity between users
  • Driver for recommendation
  • Computation reduction techniques
  • Search based methods / Item to item collaborative filtering
  • Vulnerability of recommender systems
  • Workflow from data to deployment
  • Data nuances
  • Mindsets of modelling
  • Topics
  • Elements of Classification Tree - Root node, Child Node, Leaf Node, etc.
  • Greedy algorithm
  • Measure of Entropy
  • Attribute selection using Information Gain
  • Implementation of Decision tree using C5.0 and Sklearn libraries
  • Topics
  • Encoding Methods
  • OHE
  • Label Encoders
  • Outlier detection-Isolation Fores
  • Predictive power Score
  • Topics
  • Recurcive Feature Elimination
  • PCA
  • Topics
  • Splitting data into train and test
  • Methods of cross validation
  • Accuracy methods
  • Topics
  • Bagging
  • Boosting
  • Random Forest
  • XGBM
  • LGBM
  • Topics
  • Deciding the K value
  • Building a KNN model by splitting the data
  • Understanding the various generalization and regulation techniques to avoid overfitting and underfitting
  • Kernel tricks
  • Lasso Regression
  • Ridge Regression
  • Topics
  • Artificial Neural Network
  • Biological Neuron vs Artificial Neuron
  • ANN structure
  • Activation function
  • Network Topology
  • Classification Hyperplanes
  • Best fit “boundary”
  • Gradient Descent
  • Stochastic Gradient Descent Intro
  • Back Propogation
  • Intoduction to concepts of CNN
  • Topics
  • Sentiment Extraction
  • Lexicons and Emotion Mining
  • Topics
  • Probability – Recap
  • Bayes Rule
  • Naive Bayes Classifier
  • Text Classification using Naive Bayes
  • Topics
  • Introduction to time series data
  • Steps of forecasting
  • Components of time series data
  • Scatter plot and Time Plot
  • Lag Plot
  • ACF - Auto-Correlation Function / Correlogram
  • Visualization principles
  • Naive forecast methods
  • Errors in forecast and its metrics
  • Model Based approaches
  • Linear Model
  • Exponential Model
  • Quadratic Model
  • Additive Seasonality
  • Multiplicative Seasonality
  • Model-Based approaches
  • AR (Auto-Regressive) model for errors
  • Random walk
  • ARMA (Auto-Regressive Moving Average), Order p and q
  • ARIMA (Auto-Regressive Integrated Moving Average), Order p, d and q
  • Data-driven approach to forecasting
  • Smoothing techniques
  • Moving Average
  • Simple Exponential Smoothing
  • Holts / Double Exponential Smoothing
  • Winters / HoltWinters
  • De-seasoning and de-trending
  • Forecasting using Python and R

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FAQs

Listed below are the five most popular algorithms that all data scientist should know (we cover all of these):

  • Logistic Regression
  • Naive Bayes
  • K-Nearest Neighbours
  • Support Vector Machines
  • Random Forest

No! Just a basic laptop should be sufficient for most of your personal projects.

Big Data refers to the enormous amount of data with various formats (structured, unstructured, semi-structured) generated from a variety of data sources or channels.

Data Analysis is the process of collecting and organizing raw data with the purpose to extract helpful information from it.

Data Science is a blend of various tools, algorithms, and machine learning principles for gaining useful insights from raw data. It involves designing and constructing data modelling and other data-centered operations such as preprocessing, data cleaning, analysis, etc.

Here are a few datasets sources you can rely on:

  • Kaggle
  • Socrata
  • Non-profit research group websites

This data science course is the most comprehensive, relevant, and contemporary, meeting all the present demands of the Data Industry. Don’t expect it to be some repurposed or repackaged content of redundant archaic course materials.

What’s more is that we continually upgrade the content of this course with the changes in technology, trends, and demands to provide you the best learning resource.

User

Jonathan Campbell

  • 72 Videos
  • 102 Lectures
  • Exp. 4 Year

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Item Reviews - 3

Pooja Rani

4.9

"I have become a fan of the course trainer. He is helpful, professional, and knowledgeable, doing his best in clearing all my doubts. I can gladly say that learning from Kode Campus is worth your time and money! "

Mubashshir Ali

3.7

" Whether you want to learn AI, machine learning, or data science, Kode Campus is simply the best platform for data-based courses. This data science course's presentation is really commendable. After completing this course, I've been able to switch from a Hadoop Developer to a Big Data Engineer with a good salary hike. "

Ankit Kumar Patel

4.2

" I loved the curated study materials that you get access to after joining this course. The projects and assignments were based on real-life and had a great balance of challenge and fun. Whatever the questions I raised were also resolved quickly, helping me to complete the course in time. "

S Naveen Sunny

4.2

" Hands down, Kode Campus is the best platform for e-learning. I started here with a digital marketing course but then decided to try data-based courses to expand my skillset. This course is top-notch, and the instructors are not only qualified and knowledgeable but also extend complete help whenever asked for. "

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Datascience Training
Course Features
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  • Free Trial 7 Days
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Course Features

  • Student Enrolled:1740
  • lectures:10
  • Quizzes:4
  • Duration:60 hours
  • Skill Level:Beginner
  • Language:English
  • Assessment:Yes
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