For Enquiry Dial 90-350-37-886

Interested To Start
Data Science Training?

Data Science(ML, AI, Python, Tensor flow, Keras & SAS) Training With Placement

Trained Candidates
+ 25000
Training Duration
+ 75 Hrs
Assignment Duration
+ 30 hrs
Corporate Trainings Conducted
+ 12

Why choose Ascent Software for Data Science Course In Bangalore?

Achievements after completion of Data Science Course

Training Methodology

THEORY IMAGE (1)

Data Science - Syllabus

Best-in-industry, strategically designed Course Content, Projects, Class Sessions to
accomplish the changing requirement of market

  • What is Data Science?
  • What is Machine Learning ? 
  • What is Artificial Intelligence? 
  • Role of Python in Data Science
  • What is Deep Learning?
  • SAS and Data Science
  • Data Analytics and its types
  • Basics Statistics:
  • Descriptive statistics and inferential statistics
  • Measure of central tendency -Mean, Median and Mode
  • Measure of Dispersion-Range, Variance, standard deviation and coefficient of variation
  • Frequency distribution
  • Introduction to Probability
  • Practice Session & Assignments
  • What is Python?
  • Role of Python in Data Science
  • Installing Python
  • Python IDEs
  • Jupyter Notebook Overview
  • Impementation of Advance Python techniques in Data Science
  • What is Python & History?
  • Installing Python & Python Environment
  • Basic commands in Python
  • Data Types & Operators
  • Data Structures in python- List, tuples, dictionary and sets
  • Python packages – math, Numpy, Pandas, Matplotlib, seaborn, scikit learn Loops- for loop do while
  • User Defined Functions
  • Data importing
  • Working with datasets
  • Manipulating the data sets
  • Subset the data
  • Sort the data
  • Creating new variables
  • Bins creation
  • Identifying & removing duplicates
  • Exporting the datasets into external files
  • Data Merging
  • Pivot table analysis
  • Data visualization through matplotlib, seaborn
  • Histogram
  • Bar Plot
  • Pie Chart
  • Scatter Matrix Pandas
  • Scatter matrix Violin
  • Plots
  • Line Graphs
  • Taking a random sample from data
  • Descriptive statistics
  • Central Tendency
  • Variance
  • Quartiles
  • Percentiles
  • Box Plots
  • Graphs
  • Visualization case study with poke man data
  •  Discreate Distribution
    • Bi-nominal distribution
    • Poisson distribution
    • Multinomial distribution
  • Continuous distribution:
    • Normal distribution
    • T-student distribution
    • Exponential distribution
    • Chi- square distribution
    • F- distribution
    • Anova
  •  

1.      Random sampling:

  • Sample with replacement
  • Sample without replacement
  • Training, testing and hold out dataset
2.       Stratified sampling

3. Sequential or systematical sampling

4. Clustering sampling techniques

    • What is Hypothesis testing
    • Need of hypothesis testing
    • Null hypothesis testing
    • Alternative hypothesis testing
    • Use case to solve the hypothesis testing
  • Data sanity checks
  • Anomalies detection
  • Missing Value detections & treatments
  • Project on Data handling
  • Data exploration
  • Data validation
  • Missing values identification
  • Outliers Identification
  • Data Cleaning
  • Basic Descriptive statistics
  • EDA analysis
  • Generating the insights
  •  
  • Correlation
    • Pearson correlation
    • Rank Correlation
  • VIF/Multi collinearity
  • PCA
  • Chi-Square Technique
  • Information value
  • Cluster based method
  • Tree based method
  • Lasso regression method
  • Stepwise regression method
  • Introduction to Machine Learning
  • Supervised Learning 
  • Un-supervised Learning

 

  • Supervised learning -Regression

    • Linear Regression
    • Multiple linear Regression
    • Rigid Regression
    • Lasso Regression
    • Elastic Net Regression
    • Polynomial Regression
    • Time series Analysis :

    • Need of time series
    • Moving average method
    • Holt-winter method
    • ARIMA method
    • Model Evolution metrics
    • Use case with Regression models-Project and Assignments

    Supervised Learning -Classification

    • Logistic Regression
    • Decision Tree
    • Decision Tree Regressors
    • Decision Tree Classifier
    • Naive Bayes
    • KNN
    • KNN-Regressors
    • KNN-Classifiers- Binary labels and multi labels
    • Support Vector Machines
    • Support vectors-Regressors
    • Support vectors-Classifiers
    • Ensemble learning
    • Bagging
    • Boosting
    • Random Forest
    • Random Forest -Regressor
    • Random Forest-Classifier
    • Extra Tree Network
    • Model Elevation metrics
  • Clustering Analysis
  • Hierarchical Clustering
  • Agglomerative Clustering
  • Non-Hierarchical Clustering K-Means
    • How to validate a model?
    • What is a best model?
    • Types of data
    • Types of errors
    • The problem of over fitting
    • The problem of under fitting
    • Bias Variance Tradeoff
    • Cross Validation
    • Boot Strapping
    • Neural Networks Introduction
    • Neural Network Intuition
    • Neural Network and vocabulary
    • Neural Network algorithm
    • Math behind Neural Network algorithm
    • Building the Neural Networks
    • Validating the Neural network model
    • Neural Network applications
    • Image recognition using Neural Networks
    • What is Text mining
    • Corpus
    • Tokenizer
    • POS
    • Named Entry recognizers
    • Lemmatization
    • NLTK
    • Text cleaning
    • Words Cleaning
    • Stop words
    • Cleaning Twitter Data
    • Sentimental Analysis
    • Text blob
    • Word2Vec
    • Spelling correction
    • TFIDF
    • Use Case with Text mining Analysis

1.      Overview of Deep Learning by using keras and Tensor flow

  1. Tensor flow
    • Introduction to Tensor flow
    • Constant
    • Place holders
    • Variables

3.      Multi layers Neural Networks

  • Neurons
  • Weights
  • Activations
  • Networks of Neurons
  • Training Networks
  • Back propagation
  • Gradient Descent

4. CNN

  • Feature learning
    1. Convolution
    2. Pooling
  • Classification learning
  • Flatten
  • Fully Connected
  • SoftMax

    5. DNN

   6. Digit Recognizer Classification

  • Introduction to SAS
  • Base SAS environment:
  • Interactive Vs Batch Mode
  • Elements of SAS Software Interface
  • SAS Program Editor
  • Output Window
  • Log Window
  • Other – Explorer and Result Window
  • Components on Base SAS
  • Data Management Facility
  • Programming Language
  • Data Analysis & Reporting Facility
  • SAS Data Libraries
    • Managing SAS Data Libraries
    • SAS Variable Values and Names
    • SAS Date Values

Missing Date Values

  • Reading, Writing and Sub-setting Data
  • SAS Functions
  • Mathematical functions
  • String Functions
  • Date Functions
  • Format conversion functions
  • Random number generators
    • Data Import & Export
    • Data Validation
    • Data Visualization
    • SQL Query
    • Sorting
    • Data Step merge
    • SQL merge
    • Conditional Programming
    • Transpose
    • Do loop and Array
    • SAS Macros
    • SAS Modeling Procedures
    • Project on Data handling
    • Data exploration
    • Data validation
    • Missing values identification
    • Outliers identification
    • Data Cleaning
    • Basic Descriptive statistics
    • EDA analysis
    • Generating the insights
    • Presentation the insights
  • Business understanding-Credit cards and Telecom
  • Data requirement
  • Data cleaning
  • EDA and insight generation
  • Variable creation
  • Variable reduction
  • Model Building
  • Validation Building
  • Recommendation to clients

To Enquire for Placement Related Queries
CALL 9035037886

Share this page

Learn At Home With Ascent Software

We provide same level of guidance in Online training as in classroom training. You can enquire anytime to get complete details about the courses. Our career counsellors are well trained in industry required technologies and placements.

#We are rated as "Best Online-Training Provider"

Highlights of Data Science Training

Data Science and Python

EDA Analysis & Variable Reduction Techniques

Neural Networks &Natural Language Processing

Machine Learning & Deep Learning

SAS Data Handling Project

SAS to Python Migration Project

Final Project using ML, AI, Python and SAS

Live Projects & Interview Preparation

Meet Our Industry Expert Trainers

# Certified Trainers
# 10+ Years of Industry Experience
# Study Materials Designed On Real Time Problems
# Excellent Communication
# Expert Interview Panel
# Corporate Trainings

Image Source Trainee

Call us: 080-4219-1321 hours: 8am-9pm

The focus is on In-Depth Practical Knowledge with a division of 30% Theory and 70% Practical sessions. Weekdays and weekend batches are available.

We have best working professionals who are certified and have current industry knowledge to cater the needs of students.

The program is focused to make a candidate get aware of industry requirement. Classes are followed with interview questions with are very important to crack an interview.

Covering up the course a person can easily crack an interview and can work on any real time projects as focus is more on practical training.  An Industry Recognised Course Completion Certificate is a part of program.

Each topic is covered In-depth with Theory and Practical sessions. Training sessions are covered using Presentations followed by Assignments to enhance the knowledge of  students.

We have separate Internship Programs for Final Year students and Trainee Professionals which includes projects under Certified Trainer guidance . It also includes Internship Completion Certificate.

Ascent Software Testing Training Institute

Internship Programs

Our Hiring Partners For Placements

Data Science Training - Batch Schedule

Mon-Fri | 8 AM to 10 AM | 12 AM to 2 PM

Sat- Sun | 8 AM to 10 AM | 12 AM to 2 PM

Mon-Fri | 6 PM to 8 PM | 7 PM to 9 PM

Need Different Timings ?

Enquire for Other Batch Timings

CALL : 9620983072 | 9035037886

Still Hunting For Job?

Ascent Software Certification is Accredited by all companies in the world

Get Certified
And Get Job with our Placement
Assistance Support

To Enquire for Placement Related Queries
CALL 9035037886

FAQ

Most frequent questions and answers

Ascent Software provides all necessary modes of training 

  • Classroom Training
  • Live Instructor LED Online Training
  • One to One training
  • Fast Track Training
  • Customized Training
  • Corporate Training

No worries. We at Ascent Software assures that a student should get full advantage of every session and if a class is missed that there is always a provision of backup class. We have different batches for the same course so the student is free to attend the same topic in any other batch within the stimulated course duration. If a student is unable to undersatnd certain topic then also the same process can be done.

A student can book a slot for free demo class as per his convenient timing. We have both classroom and online demo classes.

After completion of course a student will recieve globally recognized Ascent Software Training Institute Course Completion Certificate.

We accept all kinds of payment options. Cash, Card, NetBanking, Paytm, Google Pay, PhonePe etc.

You can call on 080-42191321 or you can enquire at hr@ascentcourses.com

Working hours

Monday - Saturday : 8:00-19:30 Hrs
(Phone until 20:30 Hrs)
Sunday - 8:00 -14:00

We are here

100 FT Ring Road, BTM 1st Stage, Bangalore-29
Phone: 080-42191321
Mob : 9035037886
Email: hr@ascentcourses.com

Get Update on Latest Courses