Name: LEARN DATA SCIENCE
Brand: Akswave
Rating: 4.9 (1400 reviews)

Course Content

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Module 1 | Introduction to Data Science

1) Introduction to Data Science

What is Data Science?
Why Data Science?
How to identify if a Problem needs to be solved using Data Science?
What is a Problem Statement?
Setting up the Problem Statement and Developing the Understanding of it
What are the Domains in which Data Science can be applied?
What is Machine Learning, Deep Learning, Artificial Intelligence and differences between them?
What are the requirements of Data Science?
Who can be a Data Scientist?
What is Data Analytics? Does it come under Data Science?
What are the Job Functions for Data Science, how is the Pay across the domains, how much experience is needed, etc.

Module 1.1 | Statistics for Data Science

2) Exploring the Fundamentals of Statistics : Part 1

What is Stastics?
Scales of Measurement
Types of Data

3) Basic Statistical Terminology

Population and Sample
Census and Survey
Parameter and Statistic

5) Types of Statistics

Descriptive and Inferential Statistics
Prescriptive Statistics
Predictive Statistics

6) Sampling Techniques

Convenience Sampling
Random Sampling
Systematic Random Sampling
Stratified Sampling
Cluster Sampling

7) Central Tendency and its Measures

Mean
Median
Mode
Variance
Standard Deviation
Corelation and Covariance

8) Exploring the Fundamentals of Statistics : Part 2

Percentiles/Quantiles
Quartiles

10) Distance Metrics

Average Distance from Mean
Mean Absolute Distance from Mean (MAD)
Root Mean Squared Distance from Mean (RMSD)
Z-Score

Module 1.2 | Probability for Data Science

11) Fundamentals of Probability: Part 1

Understanding what Probability is
Difference between Probability and Statistics

12) Applications of Probability in Real Time

Weather Forecast
Profit Estimation in a company
Actuarial Analysis in Insurance Business
Gaming
Manufacturing/Aerospace Engineering
Risk Evaluation
Effectiveness of a Pharmaceutical Drug

13) Probability Terminologies

Sample Space
Event
Mutually Exclusive Event
Inclusive or Independent Event
Complementary Event

14) Calculating the Probability Types

Marginal Probability
Joint Probability
Union Probability
Conditional Probability

15) Deep Dive into Probability

Bayes Theorem

17) Probability Distributions

Binomial Distribution
Normal (Gaussian) Distribution
Central Limit Theorem and Confidence Interval
Standard Error, Confidence Interval and Sample Size

18) Hypothesis Testing

Alternate Hypothesis
Null Hypothesis
Critical Region
p-value Rejection or acceptance criteria of Hypothesis
Degree of Freedom
t-Distribution and sample t-test
One Sample T-Test
Two Sample T-Test
Chi-Squared Distribution,Chi-Squared Test of Independence and F-Test

19) ANOVA (Analysis of Variance)

ANOVA (Analysis of Variance)

Module 2 | Python for Data Science

20) Python for Data Science

Introduction to various datatypes for better understanding how the data works in Python
Indexing in Python
Type Casting in Python
Loops in Python
Python Data Structures: Dataframes, Lists

21) Overview of Pandas and Numpy

Understanding the basics of Pandas
Understanding the basics of Numpy

22) Exploratory Data Analysis and Data Cleansing - Pandas

Exploring the Datasets with Pandas for Data Analysis
Applying Techniques to handle different kinds of data

23) Exploratory Data Analysis and Data Cleansing - Numpy

Exploring the Datasets with Pandas for Data Analysis
Applying Techniques to handle different kinds of data

Module 3 | Storytelling with Data

24) Data Visualization and Art of Story Telling with Data

Visualization using matplotlib, seaborn, Plotly

26) Bar Graph

Seaborn:Distribution Plots
Distplot
Heatmaps (Correlation plot using Heatmap)
Plots for Categorical Data"
Which is the best plot for deriving the business insights from the Data?

Module 4.1 | Deep Dive into Machine Learning: Regression

27) Deep Dive into Machine Learning

What is Machine Learning?
Practical Applications of Machine Learning in Real World
Regression
What and Why of Regression?
Understanding the Problem Statement in Regression
Recap of the Types of Variables in Problem Statement
Data Preprocessing for Regression
Data Cleansing
Exploratory Data Analysis
Deriving Insights using Data Transformation and Data Visualization
Feature Importance and Feature Engineering
Preparing the Data for Regression Model
Understanding Bias and Variance in the Data

28) Defining the Error Metrics

MSE
RMSE
MAE
MAPE
Assumptions of Linear Regression Models
Machine Learning Models

29) Simple Linear Regression

Defining the Problem Statement and assuming data is linear
Understanding y=mx + c
EDA and Data Preparation
Data Visualization
Model Building
Predicting on Unknown Data
Validating the results using different error metrics

30) Multiple Linear Regression

Defining the Problem Statement and assuming data is linear
Understanding y=m1x1 + m2x2 + ..... + mnxn + c
EDA and Data Preparation
Data Visualization
Model Building
Predicting on Unknown Data
Validating the results using different error metrics

31) Support Vector Regressor

Defining the Problem Statement
EDA and Data Preparation
Data Visualization
Model Building and Hyperparameter Tuning
Predicting on Unknown Data
Validating the results using different error metrics

32) Random Forest Regressor

Defining the Problem Statement
EDA and Data Preparation
Data Visualization
Model Building and Hyperparameter Tuning
Predicting on Unknown Data
Validating the results using different error metrics

33) Ridge Regression

Why Ridge Regression? Should it be used all the time?

34) Lasso Regression

Why Ridge Regression?
Should it be used all the time?

Module 4.2 | Deep Dive into Machine Learning: Classification

35) Deep Dive into Machine Learning

Deep Dive into Machine Learning

36) Introduction to Machine Learning (Classification)

What is Machine Learning?
Practical Applications of Machine Learning in Real World
Classification
What and Why of Classification?
Understanding the Problem Statement in Classification
Recap of the Types of Variables in Problem Statement
Data Preprocessing for Classification
Data Cleansing

37) Exploratory Data Analysis

Deriving Insights using Data Transformation and Data Visualization
Feature Importance and Feature Engineering
Preparing the Data for Classification Model
Understanding Bias and Variance in the Data

38) Defining the Error Metrics: (Confusion Matrix)

Accuracy
Precision
Recall
F1 Score
Entropy

39) AUC and ROC Curves, R-Squared, Adjusted R-Squared

AUC and ROC Curves, R-Squared, Adjusted R-Squared

41) Logistic Regression

Defining the Problem Statement
EDA and Data Preparation
Data Visualization
Model Building and Hyperparameter Tuning
Predicting on Unknown Data
Validating the results using different error metrics

42) Decision Trees

Defining the Problem Statement
EDA and Data Preparation
Data Visualization
Model Building and Hyperparameter Tuning
Predicting on Unknown Data
Validating the results using different error metrics

43) Random Forest

Defining the Problem Statement
EDA and Data Preparation
Data Visualization
Model Building and Hyperparameter Tuning
Predicting on Unknown Data
Validating the results using different error metrics

44) Support Vector Classifier

Defining the Problem Statement
EDA and Data Preparation
Data Visualization
Model Building and Hyperparameter Tuning
Predicting on Unknown Data
Validating the results using different error metrics

45) KNN Classifier

Defining the Problem Statement
EDA and Data Preparation
Data Visualization
Model Building and Hyperparameter Tuning
Predicting on Unknown Data
Validating the results using different error metrics

46) Ensemble Learning

Bagging
Boosting: Gradient Boosting, XGB, AdaBoost
Each of these will be used as a part of Bagging/Boosting along with other ML Algorithms:
Defining the Problem Statement
EDA and Data Preparation
Data Visualization
Model Building and Hyperparameter Tuning
Predicting on Unknown Data
Validating the results using different error metrics

47) Clustering and Segmentation

Kmeans Clustering

Module 5 | Text Mining

56) SVD - Singular Value Decomposition

SVD - Singular Value Decomposition

59) Web Scraping - Getting the real world data from a website

Do's and Don'ts of Webscraping
Understanding the Characters and metadata from the website
Preparing the code for Web Scraping
Analysing the data's quality for data analysis
Cleaning the data for Data Analysis
Performing the Sentiment Analysis

Module 6 | Deep Learning

61) Introduction to Neural Networks

Introduction to Neural Networks

62) Why Machine Learning alone is not enough for Neural Networks?

Why Machine Learning alone is not enough for Neural Networks?

63) Epoch

Back Propagation & Mini Batch
Batch Normalization
Weights Initialization
Learning Rate

66) Simple Neural Network

Defining the Problem Statement
EDA and Data Preparation
Data Visualization
Model Building and Hyperparameter Tuning
Predicting on Unknown Data
Validating the results using different error metrics

67) Dense Neural Network

Defining the Problem Statement
EDA and Data Preparation
Data Visualization
Model Building and Hyperparameter Tuning
Predicting on Unknown Data
Validating the results using different error metrics

68) Brief of Convolutional Neural Networks

Brief of Convolutional Neural Networks

69) Brief of Reccurrent Neural Networks

Brief of Reccurrent Neural Networks

Module 7 | PySpark and SparkSQL

70) PySpark - Introduction to PySpark

Intro to the Spark Architecture
When Spark is available, what is the need for PySpark?

72) Data Manipulation using PySpark and SparkSQL

Data Manipulation using PySpark and SparkSQL

73) Why traditional SQL is not enough?

Why traditional SQL is not enough?

Module 8 | Case Study and Model Deployment

Modes of Training

Classroom Training

Lives interactive sessions delivered in our classroom by our expert trainers with real-time scenarios.

Online Training

Learn from anywhere over internet, joining the live sessions delivered by our expert trainers.

Self-Pace Training

Learn through pre-recorded video sessions delivered by experts with your own pace and timings

For Coporate Training, We provide customized content and delivered by industry experts with complete practical demonstration, discussions and exercises based on practical use cases.

Our Key Highlights

Unique Benefits included in this training

BEST TRAINER : Having 12+ yrs exp and delivered more than 80 batches
QUALITY CONTENT : More content including advance features covered better in Industry
ONLINE LAB : Online Server access provided for doing your LAB practices
BEST PRICE : Affordable and best competitive price in the market
RECORDINGS : High-quality sessions recordings access for referring multiple times
REALTIME SCENARIOS : Training contains projects on Real-time Scenarios to gain more confidence

Upcoming Batches

CLASSROOM TRAINING

This Course Includes:

Delivered by our experts having 20+ yrs exp
13 Live classroom sessions
Reading material and Lab activity guide
One-to-one dedicated server access for practice
30 Hrs of Lab practices
100% practical-oriented classes
Real-time projects and certification guidance
Get certificate on course completion
Job assistance
Certified technical assistance

26-Dec

8:30am to 10:00am IST

60 Days (Mon-Fri)

31-Dec

8:30am to 10:00am IST

60 Days (Mon-Fri)

03-Jan

8:30am to 10:00am IST

60 Days (Mon-Fri)

₹35,000/- ₹22,000/- 37%OFF

ONLINE TRAINING

This Course Includes:

Delivered by our experts having 20+ yrs exp
13 Virtual online sessions
Access for 20 Hrs of Recorded videos
Reading material and Lab activity guide
24x7 dedicated online AksWave server access
30 Hrs of Lab practices
100% practical-oriented classes
Real-time projects and certification guidance
Get certificate on course completion
Job assistance
Technical support thru chat and email

25-Dec

7:00pm to 8:30am IST

60 Days (Mon-Fri)

31-Dec

7:00pm to 8:30am IST

60 Days (Mon-Fri)

03-Jan

7:00pm to 8:30am IST

60 Days (Mon-Fri)

38 % OFF ₹ 40,000/- $295 ₹25,000/-

SELF-PACED LEARNING

This Course Includes:

Access for 20 Hrs of Recorded videos
Reading material and Lab activity guide
24x7 dedicate online AksWave server access
30 Hrs of Lab practices
Real-time projects and certification guidance
Technical support thru chat and email

₹18,000/-

Our Instructor

Gurmeet S

Data Science Professional

4.8 Instructor Rating
480 Reviews

Gurmeeth is a Data Science professional with more than 5+ years in the Industry and has worked on several Consulting and Research projects across multiple domains such as Customer Fraud Detection, Insurance areas. He is passionate in sharing his knowledge on Data Science and has done so far with more than 1000+ students. He is good at translating the complex concepts into Simple and Crisp ideas to help the Students understand the Problem Statements and come up with ideas over solving them without Human Bias.

LEARN DATA SCIENCE