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Data science Syllabus
Python
Module
Programming Basics & Environment Setup
Programming Basics & Environment Setup
2 Live Sessions
1 Assessment
Topics:
- Installing Anaconda Anaconda Basics and Introduction
- Get familiar with version control, Git and GitHub.
- Basic Github Commands.
- Introduction to the Jupyter Notebook environment.
- Basics Jupyter notebook Commands.
- Programming language basics.
Module
Strings, Decisions & Loop Control
Strings, Decisions & Loop Control
2 Live Sessions
1 Assessment
Topics:
- Working With Numbers, Booleans and Strings, String types and formatting, String operations
- Simple if Statement, if-else Statement
- if-elif Statement.
- Introduction to while Loops, for
- Loops, Using continue and break
Module
Python Data Types
Python Data Types
2 Live Sessions
1 Assessment
Topics:
- List, Tuples, Dictionaries
- Python Lists, Tuples, Dictionaries
- Accessing Values, Basic Operations
- Indexing, Slicing, and Matrixes
- Built-in Functions & Methods
- Exercises on List, Tuples And Dictionary
Module
Functions And Modules
Functions And Modules
2 Live Sessions
1 Assessment
Topics:
- Introduction To Functions
- Defining & Calling Functions
- Functions With Multiple Arguments
- Anonymous Functions - Lambda
- Using Built-In Modules, User-Defined
- Modules, Module Namespaces,
- Iterators And Generators
Module
File I/O And Exceptional Handling and
Regular Expression
File I/O And Exceptional Handling and
Regular Expression
2 Live Sessions
1 Assessment
Topics:
- Opening and Closing Files, open Function, file Object Attributes, close() Method, Read, write, seek.
- Exception Handling, try-finally Clause
- Raising an Exceptions, User-Defined Exceptions
- Regular Expression- Search and Replace
- Regular Expression Modifiers
- Regular Expression Patterns
Module
Data Analysis Using Numpy
Data Analysis Using Numpy
2 Live Sessions
1 Assessment
Topics:
- Introduction to Numpy. Array Creation, Printing Arrays, Basic Operation - Indexing, Slicing and Iterating, Shape Manipulation - Changing shape, stacking and splitting of array
- Vector stacking, Broadcasting with Numpy, Numpy for Statistical Operation
Module
Data Analysis Using Pandas
Data Analysis Using Pandas
2 Live Sessions
1 Assessment
Topics:
- Pandas : Introduction to Pandas
- Importing data into Python
- Pandas Data Frames, Indexing Data Frames ,Basic Operations With Data frame, Renaming Columns, Subsetting and filtering a data frame.
Module
Data Visualization using Seaborn
Data Visualization using Seaborn
2 Live Sessions
1 Assessment
Topics:
- Seaborn: Intro to Seaborn And Visualizing statistical relationships, Import and Prepare data. Plotting with categorical data and Visualizing linear relationships.
- Seaborn Exercise
Module
Exercises
Exercises
2 Live Sessions
1 Assessment
Topics:
- 3 Case Study on Numpy, Pandas
- 1 Case Study on Pandas And Seaborn
Statistics
Module
Introduction to Statistics
Introduction to Statistics
2 Live Sessions
1 Assessment
Topics:
- Variable and its types
- Quantitative, Categorical, Discrete, Continuous
- Outliers, Causes of Outliers, How to treat Outliers, I-QR Method and ZScore Method
Module
Fundamentals of Math and Probability
Fundamentals of Math and Probability
2 Live Sessions
1 Assessment
Topics:
- Probability distributed function & cumulative distribution function.
- Conditional Probability, Baye's Theorem
- Problem solving for probability assignments
- Random Experiments, Mutually Exclusive Events, Joint Events, Dependent & Independent Events
Module
Inferential Statistics
Inferential Statistics
2 Live Sessions
1 Assessment
Topics:
- Central Limit Theorem
- Point estimate and Interval estimate
- Creating confidence interval for population parameter
Module
Descriptive Statistics
Descriptive Statistics
2 Live Sessions
1 Assessment
Topics:
- Measures of Central Tendency – Mean, Median and Mode
- Measures of Dispersion – Standard Deviation, Variance, Range, IQR (Inter-Quartile Range)
- Measure of Symmetricity/ Shape – Skewness and Kurtosis
Module
Inferential Statistics
Inferential Statistics
2 Live Sessions
1 Assessment
Topics:
- Characteristics of Z-distribution and T-Distribution.
- Type of test and rejection region
- Type of errors in Hypothesis Testing
Module
Hypothesis Testing
Hypothesis Testing
2 Live Sessions
1 Assessment
Topics:
- Type of test and Rejection Region
- Type o errors-Type 1 Errors, Type 2 Errors. P value method, Z score Method. The Chi-Square Test of Independence.
- Regression. Factorial Analysis of Variance. Pearson Correlation Coefficients in Depth. Statistical Significance
- Null and Alternative Hypothesis Onetailed and Two-tailed Tests, Critical Value, Rejection region, Inference based on Critical Value
- Binomial Distribution: Assumptions of Binomial Distribution, Normal Distribution, Properties of Normal Distribution, Z table, Empirical Rule of Normal Distribution & Central Limit Theorem and its Applications
Machine Learning
Module
Data Preprocessing
Data Preprocessing
2 Live Sessions
1 Assessment
Topics:
- Types of Missing values (MCAR, MAR, MNAR), Methods to handle missing values
- Outliers, Methods to handle outliers: IQR Method, Z Method
- Feature Scaling: Definition , Methods: Absolute Maximum Scaling, Min-MaxScaler, Normalization, Standardization, Robust Scaling
Module
Logistic Regression Model
Logistic Regression Model
2 Live Sessions
1 Assessment
Topics:
- Definition. Why is it called the “Regression model”?
- Sigmoid Function, Transformation & Graph of Sigmoid Function
Module
Evaluation Metrics for Classification
Evaluation Metrics for Classification
2 Live Sessions
1 Assessment
Topics:
- Misclassification, TPR, FPR, TNR, Precision, Recall, F1 Score, ROC Curve,and AUC. Using Python library Sklearn to create the Logistic Regression Model and evaluate the model created model
Module
Decision Tree Model
Decision Tree Model
2 Live Sessions
1 Assessment
Topics:
- Definition, Basic Terminologies, Tree Splitting Constraints, Splitting
- Splitting Methods: - GINI, Entropy, Chi-Square, and Reduction in Variance
- Using Python library Sklearn to create the Decision Tree Model and evaluate the model created
Module
Random Forest Model
Random Forest Model
2 Live Sessions
1 Assessment
Topics:
- Ensemble Techniques: Bagging/bootstrapping & Boosting.
- Definition of Random Forest, OOB Score
- K-Fold Cross-Validation
Module
Naive Baye’s Model
Naive Baye’s Model
2 Live Sessions
1 Assessment
Topics:
- Definition, Advantages, Baye's Theorem Applicability, Disadvantages of Naive Baye's Model, Laplace's Correction, Types of Classifiers: Gaussian, Multinomial and Bernoulli
- Using Python library Sklearn to create the Naive Baye's Model and evaluatethe model created
Module
K Means and Hierarchical Clustering
K Means and Hierarchical Clustering
2 Live Sessions
1 Assessment
Topics:
- Definition of Clustering, Use cases of Clustering
- K Means Clustering Algorithm,Assumptions of K Means Clustering
- Sum of Squares Curve or Elbow Curve
Module
Machine Learning Exercises
Machine Learning Exercises
2 Live Sessions
1 Assessment
Topics:
- Business Case Study for Kart Mode
- Business Case Study for Random Forest
- Business Case Study for SVM
- Business Case Study for Linear Regression
- Business Case Study for Logistic Regression
- Business Case Study for KMean Cluster
Time Series
Module
Introduction to Time Series Forecasting
Introduction to Time Series Forecasting
2 Live Sessions
1 Assessment
Topics:
- Basics of Time Series Analysis and Forecasting
- Method Selection in Forecasting
- Moving Average (MA) Forecast Example
- Different Components of Time Series Data
- Log Based Differencing, Linear Regression for Detrending
Module
Introduction to ARIMA Models
Introduction to ARIMA Models
2 Live Sessions
1 Assessment
Topics:
- ARIMA Model Calculations, Manual ARIMA Parameter Selection
- ARIMA with Explanatory Variables
- Understanding Multivariate Time Series and their Structure
- Checking for Stationarity and Differencing the MTS
NLP
Module
Natural Language Processing
Natural Language Processing
2 Live Sessions
1 Assessment
Topics:
- Text Analytics
- Introduction to NLP
- Use cases of NLP algorithms
- NLP Libraries
- Need for Textual Analytics
- Applications of NLP
- Word Frequency Algorithms for NLP Sentiment Analysis
Module
Text Analysis
Text Analysis
2 Live Sessions
1 Assessment
Topics:
- Distance Algorithms used in Text Analytics
- String Similarity
- Cosine Similarity Mechanism
- The similarity between two text documents
- Levenshtein distance - measuring the difference between two sequences
Module
Understanding Keras API for implementing
Neural Networks
Understanding Keras API for implementing
Neural Networks
2 Live Sessions
1 Assessment
Topics:
- Information Retrieval Systems
- Information Retrieval - Precision,
- Recall,F- score TF-IDF
- KNN for document retrieval
- K-Means for document retrieval
- Clustering for document retrieval
Module
Text Pre Processing Techniques
Text Pre Processing Techniques
2 Live Sessions
1 Assessment
Topics:
- Need for Pre-Processing
- Various methods to Process the Textdata
- Tokenization, Challenges inTokenization
- Stopping, Stop Word Removal
Module
Stemming
Stemming
2 Live Sessions
1 Assessment
Topics:
- Stemming - Errors in Stemming
- Types of Stemming Algorithms - Table
- Lookup Approach
- N-Gram Stemmers
Module
Use cases on NLP
Use cases on NLP
2 Live Sessions
1 Assessment
Topics:
- Sentiment Analysis
- Content summarization
SQL
Module
RDBMS And SQL Operations
RDBMS And SQL Operations
2 Live Sessions
1 Assessment
Topics:
- Introduction To RDBMS
- Single Table Queries - SELECT, WHERE,ORDER BY, Distinct, And, OR
- Multiple Table Queries: INNER, SELF,CROSS, and OUTER, Join, Left Join, Right Join, Full Join, Union
Tableau
Module
Introduction to Tableau
Introduction to Tableau
2 Live Sessions
1 Assessment
Topics:
- Connecting to data source
- Creating dashboard pages
- How to create calculated columns
- Different charts
- Hands-on : -Hands on on connecting data source and data cleansing -Hands on various charts
Module
Dashboard and Stories
Dashboard and Stories
2 Live Sessions
1 Assessment
Topics:
- Working in Views with Dashboards and Stories
- Working with Sheets
- Fitting Sheets
- Legends and Quick Filters
- Tiled and Floating Layout
- Floating Objects
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Affordable Data Science Course Fees In Bangalore
We believe that our Data Scince course fees are competitive and affordable, while still providing the highest quality training and support.
Get Course Info
Get in touch for any questions or support—we’re just a message away!
Live Virtual
- 100% Job Support
- Learn 21+ Modules
- 3 Months Program
- 10+ Certifications
- Work on Live Projects
- 50+ Tools & Case Studies
Classroom
In-Person Classroom Training
- 100% Job Support
- Learn 21+ Modules
- 3 Months Program
- 10+ Certifications
- Work on Live Projects
- 50+ Tools & Case Studies
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FAQ’s
Learn from expert trainers in a state-of-the-art environment with personalized attention,
What is Data Science?
Data Science is nothing but an amalgam of methods integrating statistics, data analysis, and machine learning. A data scientist analyses processed and unprocessed data to enhance business decisions. Data Scientists must have good hands on Python, R, R Studio, Hadoop, MapReduce, Apache Spark, Apache Pig, Java, NoSQL database, Cloud Computing, etc.
Does this course cover basics?
Absolutely! At Indra’s Academy Bangalore, be it online or offline classes, we cover all the basic topics since we believe in making the fundamental concepts clear. Indra’s Academy makes sure all the candidates are pro in data science.
Who is eligible for this course?
To become a data scientist, candidates pursuing the course must have a Bachelor’s degree in Mathematics, Statistics, Computer Science or Data Science. Also, engineers from IT are also eligible to join this course.
Can I enroll for an online course?
Yes! Indra’s Academy is now offering online courses for students who cannot enroll for offline classes due to pandemic and also to offer excellent training for students all over the globe. Students from any part of the world can enroll for courses at Indra’s Academy.
Do I get job assistance at Indra's Academy?
As mentioned earlier, Indra’s Academy not only offers excellent coaching to students but also makes sure each candidate gets equal opportunity to interview in well-known organizations. We deliver 100% job assistance with a minimum of 30 interviews.
What is the duration of this course?
The duration of this course is 3 months. In these 3 months, our skilled and experienced trainers prepare candidates for complex company projects and also for tough interviews.
What is the average salary of data scientists?
Since data science is an extremely responsible and tough job, companies pay handsome salaries to skilled candidates. Initial salary ranges from 5-6 LPA.
What does the course cover?
The Data Science Course includes:
- Python and R programming
- Machine learning
- Exploratory Data Analysis
- Data Visualization
- Inferential Statistics
- Text Mining
- Deep Learning
- Predictive Modelling
- Etc
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Success Stories
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An excellent place to learn data science. The teachers are extremely knowledgeable and have a lot of industry experience. The training is structured well and covers all the fundamentals of the subject. The best part of the course is that they have live projects which they guide you through.
Suleman
Data Analyst
I was looking to learn Data Science and I came across indras academy. I started learning data science with indras academy. The course was very well designed and the entire learning experience was very professional. I would recommend indras academy to learn data science.
Surendra
Data Scientist
The syllabus is very vast and covers almost every possible topic in data science. The trainer is very helpful and explains the topics very clearly. The course material is good and there are assignments at the end of each module. I am happy with the course and the mentors.
Ganesh
Data Architect
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