Advanced Data Science Course with Artificial Intelligence in Marthahalli, Bengaluru.
Social Prachar is one of the Top Data Science Training Institute in Maratahalli, Bangalore with Placement assistance. We will provide Advanced Data Science training includes Python, Machine Learning, Statistics, Deep learning, NLP etc with Real time trainers, client case studies and live Real world projects. Data Science course with Certification from Social Prachar will help to the Candidates to get in-depth knowledge of Data Science by laying the strong foundation for the career. 700+ Trainees Rated us Best Data Science Training in Bengaluru.
Data Science is the most awaited and promising career in the 21st century with 2,00,000+ Job opportunities by 2021 with minimum 4LPA to 15LPA salaries.
Attend Free Demo on Data Science Tomorrow at 10 AM and 4 PM →
- We provide both Classroom & Online course on Advanced Data Science program.
- Social Prachar provides the Data Science course training with R–Language , Tableau, Python , Machine Learning, Deep learning , Natural Language Process, Artificial Intelligence and Big Data with Real time Projects.
The course covers technical as well as business aspects of Data Science. This will helps to gain more industrial oriented knowledge regarding Data Science and other business aspects.
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Certificate of Excellence Award for
Academy of the Year 2019 - '20
We are happy to announce that Social Prachar has been awarded as the Best Academy of the Year 2019 – 2020 @7th Asian Education Summit, Mumbai Presented by Juhi Chawla, former Miss India
Get Trained by Experts from IIIT and AMITY university
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- There is a wide variety of career opportunities available for Data Science Professionals because Data Scientists are required in different areas like Data Analytics , Data Research, Big Data and Data Management and more.
- Data Science Jobs has high range of Salaries. Minimum average salary ranges are from 5 Lakhs to 30 lakhs per annum.
Master Data Science with Certification
Call our Quick Helpline 8019 479 419 for Instant Help.
Data Science Course Highlights
Realtime Expert Trainers
Dedicated portal for Practice
Live Projects to work
Who to Join Data Science course
Data Analysts, Business Analysts,
Graduates, Post Graduates
Also, anyone having interest in Data Science is free to attend our workshop.
Data Science Job Roles Available
Jr Data Scientist
Business Intelligence Analyst
Data Mining Engineer
Senior Data Scientist
Data Science Job Market in 2020
Sexiest Job of 21st Century. Data Scientist is the Sexiest Job of the 21st Century by Harvard university
8,000+ unique job postings for data scientists each month.
5,00,000+ Skilled Data Scientists required by the End of 2020.
Salaries range from 4LPA to 15LPA.
GlassDoor ranked it top place for popular jobs
- 1 Advanced Data Science Course with Artificial Intelligence in Marthahalli, Bengaluru.
- 2 Attend Free Demo on Data Science Tomorrow at 10 AM and 4 PM →
- 3 Certificate of Excellence Award for
- 4 Academy of the Year 2019 - '20
- 5 Get Trained by Experts from IIIT and AMITY university
- 6 Upcoming Batches
- 7 Start with a FREE Session
- 8 Master Data Science with Certification
- 9 Call our Quick Helpline 8019 479 419 for Instant Help.
- 10 Data Science Course Highlights
- 11 Who to Join Data Science course
- 12 Data Science Job Roles Available
- 13 Data Science Job Market in 2020
- 14 Start with a FREE Orientation Session
- 15 Data Science course Curriculum
- 16 Call Our Career Advisor Now for Requisite Details.
- 17 Other Popular Courses
- 18 Frequently Asked Questions
- 18.1 What about Your Data Science Course Details ?
- 18.2 What type of Training Modes You Provide?
- 18.3 Who are Eligible to Learn Data Science?
- 18.4 What skills do I need to Master Data Science?
- 18.5 What is the Data Science Job Market Now ?
- 18.6 Do You Provide Job Placements ?
- 18.7 What Type of Certifications You Provide After the course ?
- 18.8 Do You Provide Hands-on Experience ?
- 18.9 What about Trainer Faculty experience ?
- 18.10 What is data Science ?
- 18.11 I am 2018 Fresher Am i Eligible to Learn Data Science course?
- 18.12 What are the Modules you cover in Data Science Course ?
- 18.13 I have 3+ Years IT experience, am i Eligible to Learn Data Science Course?
- 18.14 I'm from EEE background , can I go for data science , I don't have basics of any programming language
- 18.15 I don't have any engineering background can I still learn data Science.
- 18.16 Will u explain about the whole data science course including Python, R lang etc....?
- 18.17 Will this be best option for MBA finance students? How will it be helpful for finance students??
- 18.18 I'm weak at Mathematics, Can I opt Data Scientist as my career ?
- 18.19 I'm from Pharmacy background. Please suggest me whether am I applicable to do this course?
- 18.20 I'm from mechanical background, but i want to learn data science , is it possible to understand without knowledge in languages like c , java knowledge
- 18.21 Hello am from medical science field am I eligible for this course
- 18.22 Is there any minimum percentage criteria of graduates for jobs in data science
Data Science course Curriculum
INTRODUCTION TO STATISTICS
Basic business statistics
- How businesses use statistics
- The basic vocabulary of statistics
- The types of data used in business
Organizing and Visualizing Data
- The sources of data used in business
- To construct tables and charts for numerical data
- To construct tables and charts for categorical data
- The principles of properly presenting graphs
Numerical Descriptive Measures
- To describe the properties of central tendency, variation, and shape in numerical data
- To construct and interpret a boxplot
- To compute descriptive summary measures for a population
- To compute the covariance and the coefficient of correlation
Basic Probability and Terms
- Basic probability concepts
- Conditional probability
- To use Bayes’ Theorem to revise probabilities
- Various counting rules
- Types of Distributions like Discrete and Continuous
- Functions of Random Variables
- Probability Distribution Graphs
Sampling and Sampling Distributions
- To distinguish between different sampling methods
- The concept of the sampling distribution
- To compute probabilities related to the sample mean and the sample proportion
- The importance of the Central Limit Theorem
Confidence Interval Estimation
- To construct and interpret confidence interval estimates for the mean and the proportion
- How to determine the sample size necessary to develop a confidence interval estimate for the mean or proportion
- How to use confidence interval estimates in auditing
Fundamentals of Hypothesis Testing
- Null and Alternate hypothesis
- One sample Tests
- Test statistic and critical values
- Possible errors in testing
- p- value approach | t and z-tests | Testing on proportions
DATA TRANSFORMATION AND VISUALIZATION
Merge, Rollup, Transpose and Append | Smoothing |
Aggregation | Normalization | Attribute construction
Missing value Imputation | Outlier Analysis and Treatment |
Binning | Creating dummy variables | feature scaling |
Extracting Date | Log Transformation | Feature split | Label
Encoding | One-Hot Encoding
Exploratory Data Analysis
Summarizing and Visualizing the Important Characteristics
of Data | Visualization techniques – Box plot | Histogram |
Multi-variate chart | Pareto chart | Scatter plot | Stem-
Introduction to Python
Installation | Python Basics | Spyder IDE | Jupyter Notebook
| Floats and Strings Simple Input & Output | Variables |
Operators | Single and Multiline Comments |
Taking input from user
List | Strings | Tuple | Dictionary | Sets and their examples
If | if-else | if-elif-else | nested if else
For | while | nested loops| loop control statements
Creating user defined Functions | Function arguments like-
Required, Keyword, Default and variable-length | Scope of
variables in creating functions | Anonymous Functions –
Exception and File handling
Exception Handling | Raising Exceptions | Assertions | Files
INTRODUCTION TO MACHINE LEARNING
What is Machine Learning? | End-to-end Process of
Investigating Data Through a Machine Learning Lens |
Evolution and Trends | Application of Machine Learning |
Best Practices of Machine Learning
Machine Learning Methods
Supervised | Unsupervised
Machine Learning Algorithms
Classification | Regression | Time Series | Collaborative
Filtering | Clustering | Principal Component Analysis
MACHINE LEARNING WITH PYTHON
Basics of numpy | creating arrays | Numpy functions
Introduction to Pandas | IO Tools | Pandas – Series and Dataframe and their functions
Matplotlib and Seaborn
Graphical representation of data using various plots
Introduction to SciKit Learn | Load Data into Scikit Learn |
Run Machine Learning Algorithms Both for Unsupervised and
Supervised Data | Supervised Methods: Classification &
Regression | Unsupervised Methods: Clustering, Gaussian
Mixture Models | Decide What’s the Best Model for Every
MACHINE LEARNING APPLICATIONS
Implementing Simple & Multiple Linear Regression |
Making Sense of Result Parameters | Model Validation |
Handling Other Issues/Assumptions in Linear Regression:
Handling Outliers, Categorical Variables, Autocorrelation,
Multicollinearity, Heteroskedasticity | Prediction and
Confidence Intervals | Use Cases
Implementing Logistic Regression| Making Sense of Result
Parameters: Wald Test, Likelihood Ratio Test Statistic, Chi-
Square Test | Goodness of Fit Measures | Model
Validation: Cross Validation, ROC Curve, Confusion Matrix |
Implementing Decision Trees | Homogeneity | Entropy
Information Gain | Gini Index | Standard Deviation
Reduction | Vizualizing & Prunning a Tree | Implementing
Random Forests using Python | Random Forest Algorithm
| Important hyper-parameters of Random Forest for tuning
the model | Variable Importance | Out of Bag Errors
Bayes Theorem | Gaussian Naïve Bayes & its
implementation | Multinomial Naïve Bayes, Count
vectorizer, TF-IDF Vectorizer and its use cases on Text
Concept of nearest neighbors | Euclidean Distance | Use
Cases of KNN Classifier & Regressor
Support Vector Machine
Introduction to support vectors | Concept of hard & soft
margins | slack variable | Lagrangian Primal & Dual | Kernel
Trick | Use Cases
Introduction | Components of Time Series | Stationarity |
ACF | PACF | ARIMA model for forecasting | Use Cases
Clustering concept | Finding optimal number of clusters |
Agglomerative & Divisive Clustering | Dendrograms |
Linkage Matrix like Single, Complete & Average | Use Cases
Bagging | Boosting | Stacking | Regularization | Different
cross-validation techniques used to treat Over fitting and
Under fitting in machine learning models
Principal Component Analysis
Concept of Dimensionality reduction | Eigen values | Eigen
Vectors | Use Cases
Title: Real Estate Price Prediction using Linear Regression
Industry: Real Estate
Description: The goal of this Use-case is to make property
price predictions using Real Estate data. The dataset
contains the of the price of apartments and various
characteristics of the property. Based on this data, decide
on the price of new properties.
Title: Loan Prediction using Logistic Regression & Decision
Description: The goal is to build a classification model to
predict if a loan is approved or not. Dataset contains
demographic information like age, income etc. of various
customers. Based on this, predict for a new customer
whether loan will be approved or not
Title: Student’s Performance Prediction using KNN & Naïve
Description: The goal is to build a classification model to
predict student’s performance from various characteristics
associated with student’s academics
Title: Handwritten digit recognition using Decision Tree
Description: Dataset contains various pixel values of
handwritten digits between 0 to 9. The goal is to build a
classification model to predict the digit from its pixel
Title: Amazon fine food review-Sentiment Analysis
Description: The analysis is to study Amazon food review
from customers and try to predict whether a review is
positive or negative. The dataset contains more than 500k
reviews with number of upvotes and total votes to those
DEEP LEARNING APPLICATIONS
Understanding Neural Networks | The Biological
Inspiration | Perceptron Learning & Binary Classification |
Backpropagation Learning | Learning Feature Vectors for
Words | Object Recognition
Keras for Classification and Regression in Typical Data
Science Problems | Setting up KERAS | Di erent Layers
in KERAS | Creating a Neural Network Training Models
and Monitoring | Artificial Neural Networks
Introducing Tensorflow | Neural Networks using
Tensorflow | Debugging and Monitoring | Convolutional
Recurrent Neural Networks
Introduction to RNN | RNN long & short term dependencies
| Vanishing gradient problem | Basic LSTM | Step by step
walk through LSTM | Use cases
ANN ON KERAS
Title: Credit Default using ANN on Keras
Description: This research aimed at the case of customers’
default payments in Taiwan. From the perspective of risk
management, the result of predictive accuracy of the
estimated probability of default will be more valuable than
the binary result of classification – credible or not credible
CNN ON KERAS & TENSORFLOW
Title: Handwriting/Facial recognition using CNN on
TensorFlow & Keras
Industry: Pattern Recognition
Description: This project will help build a model using
Convolutional Neural Network to recognize
Title: Google stock price prediction
Description: Dataset contains dates, volume, open, close, high and low prices of stocks. Based on this build a LSTM model to predict current and future stock price
Computer Vision with Python:
- Introduction to OpenCV
- Core Operations
- Image Processing in OpenCV
- Feature Detection and Description
- Video Analysis
- Machine Learning
- Object Detection
- OpenCV-Python Bindings
NLP with Python:
- Introduction to NLTK
- Tokenizing words and Sentences with NLTK
- Stop words and Stemming with NLTK
- Part of Speech Tagging with NLTK
- Chunking & Chinking
- Lemmatizing with NLTK
- Wordnet with NLTK
- Converting words to Features
- Text Classification with NLTK
- Combining Algorithms with NLTK
- Creating a module for Sentiment Analysis
- Twitter Sentiment Analysis with NLTK
- Named Entity Recognition with Stanford NER Triggers
- Testing NLTK and Stanford NER Triggers for Accuracy and Speed
- Over view and basic syntax of R
- R variables and operators
- R Objects
- Data Frame
- Conditional statements of R
- If -else if-else
- Nested if-else
- Loops in R
- Repeat loop
- While loop
- For loop
- Loop control statements
- Creating user defined functions in R
- Data reshaping and File handling
- Reading excel, csv, xml files in R
- Reading Web data in R
- Connecting to Data bases in R
- Data manipulation with dplyr package(Case studies on data manipulation with real time datasets)
- Data visualization with ggplot package & case studies
- Creating Interactive Dash Boards with R markdown and Shiny
SQL (Structured Query Language)
- Introduction to SQL
- SQL Select Statements
- Execute a basic SELECT statement
- Restricting and Sorting Data
- Limit the rows retrieved by a query
- Sort the rows retrieved by a query
- Single-Row Functions
- Describe various types of functions available in SQL
- Use character, number, and date functions in SELECT statements
- Describe the use of conversion functions
- Displaying Data from Multiple Tables
- Write SELECT statements to access data from more than one table using equality and nonequality joins
- View data that generally does not meet a join condition by using outer joins
- Join a table to itself by using a self join
- Aggregating Data Using Group Functions
- Identify the available group functions
- Describe the use of group functions
- Group data using the GROUP BY clause
- Include or exclude grouped rows by using the HAVING clause
- Manipulating Data
- Describe each DML statement
- Insert rows into a table
- Update rows in a table
- Delete rows from a table
- Merge rows in a table
- Control transactions
- Creating and Managing Tables
- Including Constraints
- Describe constraints
- Create and maintain constraints
- Introduction to Tableau Software
- Tableau Worksheets
- Tableau Calculations
- Tableau sort and filter
- Tableau charts
- Tableau Advanced
- Case Study with Tableau
Call Our Career Advisor Now for Requisite Details.
Other Popular Courses
Frequently Asked Questions
We are Providing 3 Month and 6 Month Curriculums on Data Science includes 2 Real Time Projects, MNC certifications, Job support, Community Meetups, Bootcamps etc to Build your Career into data science field.
- IT Professionals,
- Data Analysts,
- Business Analysts,
- Functional Managers,
- Post Graduates
- Also, anyone having interest in Data Science
You need to get skilled on following, in order to take a deep dive in this field.
- Statistic and probability
- Programming Languages (Java, Scala ,SQL, R, Python)
- Data mining
- Machine learning
- Deep Learning
- Sexiest Job of 21st Century. Data Scientist is the Sexiest Job of the 21st Century by Harvard university
- 6,000 unique job postings for data scientists each week.
- 3,00,000+ Skilled Data Scientists required by the End of 2018.
- salaries range from Min 3L per Annum to 20 L per Annum.
- GlassDoor ranked it top place for popular jobs
Yes, We have a Dedicated HR team for Placement support Like Resume Preparations, Mock Interviews, Main Interviews, Company Tie-ups etc
We Provide Data Science Mastery Certificate from Social Prachar and Two More MNC certifications. Get In touch with the Team for more details about all our certifications.
Yes All the data science Trainees need to work on min 2 Real Time projects for Better understanding of Real Time Data Science Methods and Techniques.
Our Trainer Faculty is from Top MNC’s who has 10+ years of Experience with Real time clients. They will Mentor you all the way till you will get confidence on the subject. We have Community meetups,Boot Camps,workshops etc where you can meet the industry experts.
Data Science is the New Buzz word is the Market Right Now !
Data Science involves using automated methods to analyze massive amounts of data and to extract knowledge from them. A Data Scientist is someone who is better at Statistics than any software Engineer and better at Software Engineering than any Statistician. They are responsible for discovering insights from massive amounts of structured and unstructured data to help shape or meet specific business needs and goals.
Yes, You are Eligible to Learn Data Science Course If you are Okay to Learn Programming and Good with Mathematics and Business Knowledge
We will Start from basics Like Introduction to Data science,R,Python,Deep Learning,Statistics,Machine Learning, Hadoop, Spark, SQL Etc with Two Real Time Projects at the End for Better Understanding of Data Science.
Yes, Undoubtedly Your Profile is Eligible to Learn Data Science course.
I'm from EEE background , can I go for data science , I don't have basics of any programming language
Yes, you can if you are willing to learn python programming and good with mathematics and statistics.
All Bsc, BCom, BTech, MBA, MTech MCA are eligible to learn Data Science.
Yes, Data Science ,R,Python,and tablueau, etc
Now its booming so anyone who completed Btech/MBA/Degree etc can learn, but only if you are willing to learn programming.
Yes you can opt but need to work a lot on maths. Our team will provide you the required material to overcome.
Yes, IF, You have less than 0-2 years in Pharmacy you can learn. And also if u are willing to learn programming you can learn data science.
I'm from mechanical background, but i want to learn data science , is it possible to understand without knowledge in languages like c , java knowledge
Yes, you can learn easily. Our curriculum starts from scratch, so you can learn without much confusions.
No. If its just for learning no problem. But if u need to make a career into data science, it will be little tough.
No Minimum criteria but need to have good knowledge on Mathematics.