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Data Science(ML, AI, Python, Tensor flow, Keras & SAS) Training With Placement
- Globally Recognized Certificate
- Real Time Live Training sessions
- Corporate Trainers with 10+ Years Industry Experience
- Hands On Exposure on Real Time Projects
- Resume and Interview Preparation
- Get 100% Guaranteed Job Support
- Join for Internship Programs and get exposure to work on Industry Projects
- 3 Months Job support after completion of training
- Both Online and Offline trainings available
Why choose Ascent Software for Data Science Course In Bangalore?
- This course covers all aspects of Data Science including Ml, AI, Deep Learning, Python and SAS
- Tools used are Python, Excel, SAS, Tensorflow and Keras
- Main Focus on Hands-On Training
- Contains Real World Business problems and Examples
- Rich study material and handouts for reference
- Assignments for each topic
- One on one mentorship for each topic
- Projects included are SAS Data Handling Project, SAS to Python Migration Project
- Final Project using Machine Learning, Python and SAS
Achievements after completion of Data Science Course
- Build predictive models using linear, logistic regressionand decision trees
- Build machine learning models using Decision trees Neural sets,SVM and Random forest
- Build deep learning models using ANN, CNN and DNN
- Building chatbot models using Tensor flow and NLP
- Deep understanding and practical knowledge on Machine Learning, Deep Learning and Artificial Intelligence
- Build the machine learning models using MS-Excel
Training Methodology
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
- 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
- Convolution
- 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
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
# 10+ Years of Industry Experience
# Study Materials Designed On Real Time Problems
# Excellent Communication
# Expert Interview Panel
# Corporate Trainings
Call us: 080-4219-1321 hours: 8am-9pm
- course Duration
The focus is on In-Depth Practical Knowledge with a division of 30% Theory and 70% Practical sessions. Weekdays and weekend batches are available.
- Certified Trainers
We have best working professionals who are certified and have current industry knowledge to cater the needs of students.
- Placements
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.
- Course Benefits
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.
- Course Highligts
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.
- Internship programs
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.
Internship Programs
- 6 Months Internship for Final - Year Students
- 3 Months Internship for First, Second and Third year students
- 6 Months Internship for BE, B.Tech, BSC and other students
- Internship for Working Professionals
- Internship Program to get hands on experience
- Internship Programe Certificate Issued
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
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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