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PACE

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About PACE™ PACE™ (Professional Academy for Corporate Excellence) is a leading

training organization in the domain of Data Science and Analytics. We offer training

solutions to individuals, engineering colleges, b-schools, as well as companies.

We are an Authorized IBM CE (Career Education) partner.

You can visit the following link to find us on the IBM website:

https://www.ibm.com/in-en/services/careereducation-authorized-ibm-partners

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Why PACE™ for the Data Science course?

IBM Certification: We are an IBM CE (Career Education) Authorized partner and

upon course completion the students will receive a certificate from IBM.

Faculty: Our faculties have real time experience. This means that the content will be

covered from a practical perspective and the focus will be on how to apply their learning

in a practical, real-world environment.

Industry connect: At PACE we are renowned for our recruitment consulting expertise.

We have very strong industry connects and this means that at PACE you will find the

right opportunity waiting for you. The corporate exposure that you will get with us is

unparalleled.

Course structure: The Data Science courses that we offer have been designed by a

panel of SMEs (Subject Matter Experts) who have a strong professional and academic

record in the field. Furthermore, our curriculum is approved by the biggest names in the

industry.

Projects: At PACE we take up consulting projects from SMEs and in a simulated staging

environment. This gives the students an invaluable opportunity to dedicate their time to

learn practical skills which they can apply in a real-world environment.

Overall experience: Student experience is a core value at PACE. We believe that our

students are at the heart of everything we do. We offer not only Data Science classes but

a whole range of other courses as well. This makes for a very productive experiences for

the students.

Study mode: Both Online as well as Offline options are available.

Infrastructure: At our institute, we have hi-tech facilities which creates a

conducive, positive and dynamic learning environment for the students.

FREE Add-on Courses: These courses are offered to help you add more skills. The Data

Science students can choose to do these additional courses free of cost. Some of the

courses are: Advance Excel, Business English, and Soft Skills.

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What is Data Science?

Data science is a concept to unify statistics, data analysis, mathematics, programming

and their related methods "to understand and analyze actual phenomena" with data. It is

a multi-disciplinary discipline that is the foremost problem-solving tool of the current

industrial age. It employs techniques and theories drawn from many fields within the

broad areas of mathematics, statistics, information science, and computer science from

the sub-domains of machine learning, classification, cluster analysis, data mining,

databases, and visualization.

The Data Science Training enables you to gain knowledge of the entire Life Cycle of Data

Science, analyzing and visualizing different datasets, different Machine Learning

Algorithms like Linear regression, K-Means Clustering, Decision Trees, Random Forest,

Neural Network, different optimization techniques, and advanced concept in data science.

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Why is Data Science so popular?

1. High Salaries

From tech start-ups to Fortune 500 companies, data scientists are a hot commodity. A

McKinsey study predicts that by 2020 the U.S. could face a shortage of almost 200,000

people with “deep analytical skills.” No surprise, then, that they boast an eye-popping

average starting salary of around $120,000 per annum. The demand for data scientists is

similar in Asia.

Mean Salaries in Data Science

2. Used in all industries

Data scientists use a combination of Hadoop, Spark, R, Python, Panda, and other

programming languages to solve business challenges. With unprecedented autonomy

and respect, data scientists work across various departments gaining the confidence of

C-level executives. According to Linda Burtch, managing director at executive recruiting

firm Burtch Works,

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3. Make a difference

Data science is about more than a cushy salary and rubbing shoulders with tech elite.

Many of the best boot camps and fellowships work with government and non-profit

groups on a variety of social projects. The University of Chicago’s Data Science for Social

Good fellowship even pays fellows an $11,000 – $16,000 stipend.

4. It is a global trend

A globalized economy means cultures mix in workplaces like never before. Understanding

data science helps you speak an international language that contributes to the bottom

line from day one.

5. Industry thinks Data Scientists are sexy

The 20th century gave us alluring job titles like astronaut, stockbroker, and professional

gamer. Now there’s a new sheriff in town when it comes titillating

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Why should I do a Data Science Course?

Job opportunities: There are many jobs available in this domain and it is not surprising

why. All the largest companies in the world use some form of Data Science and this

creates a huge job opportunity for skilled individuals.

It IS the present and the future: The data age is here, and it is here to stay. Those

organizations that fail to adapt to this development are at great risk of going extinct

sooner rather than later. This means there will always be a huge demand for data

scientists.

Suitable for everyone: Unlike most IT courses, this course is suitable for anyone,

regardless of their educational background. Whether you are a postgrad, undergrad or

+12, whether you studied Science, Commerce or Arts, there is equal opportunity for

everyone.

Work from home opportunities: Perhaps the best pull of Data Science is that you can

work from home. If you have the required expertise, you will have ample opportunities

to work from anywhere.

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Why Python for Data Science?

Python offers plenty of benefits which mean that an increasing number of people are

adopting Python for their work. As one of the most popular mainstream programming

languages on the market, it’s a practical choice for tech types of all kinds – data

scientists included.

Here’s few reasons why you might choose Python for data science:

• Python is easy to use

• Python is versatile

• Python is better for building analytics tools

• Data visualization with Python is easy

• Python is better for deep learning

Who should attend?

• IT Professionals

• Statisticians

• Programmers

• Business Strategists

• This course is also suitable for freshers who have no prior experience in Data

Science and/or analytics.

Eligibility Criteria

There is no eligibility criteria for this course. However, participants are expected to have

a working knowledge of computers.

Batches

1. Morning Batch from 7AM to 8.30AM ( Mon-Fri) – 90 Hours.

2. Evening Batch from 7PM to 8.30PM ( Mon-Fri) – 90 Hours.

3. Week-end Batch from 11AM to 5PM ( Saturday & Sunday ) – 120 Hours.

4. Full Day Batch from 11AM to 5PM (Mon-Fri) – 120 Hours

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CONTENT

PART I: Python Essentials for Data Science

Module 1: Python Fundamentals and Programming

Module 2: Data Handling with NumPy and Pandas

Module 3: Data Visualization with Matplotlib

Module 4: Advanced Plotting with Seaborn

PART II: Statistics for Data Science

Module 5: Introduction to Statistics for Data Science

Module 6: Sampling methods in Statistics

Module 7: Exploratory Analysis and Distributions

Module 8: Advanced Statistics for Data Science

PART III: Data Science & Applied Machine Learning

Module 9: Machine Learning (ML) Fundamentals

Module 10: ML Regression and Classification Algorithms

Module 11: ML Advanced Algorithms & Techniques

Module 12: Deep Learning & Neural Networks

Module 13: Applications of ML in Bigdata, Could Computing etc.,

PART IV: Practice Sessions – Projects by Industry Experts

Addon Topics:

1. CV preparation, mock interviews & Guidance

2. Business English & Communication Skills

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UNIT-1: Python Essentials for Data Science

Module 1 – Python Fundamentals and Programming

• What is Python?

• Why is Python essential for Data Science?

• Versions of Python

• How to install Python

• Anaconda Distribution

• How to use Jupyter Notebooks

• Command line basics

• GitHub overview

• How to execute Python scripts from command line

• Python Data Types

• Programming Concepts

• Python, Operators

• Conditional Statement, Loops

• Lists, Tuples, Dictionaries, Sets

• Methods and Functions

• Errors and Exception Handling

• Object Oriented Programming in Python

• Modules and Packages

Module 2 – Data Handling with NumPy and Pandas

• NumPy overview

• Arrays & Matrices

• NumPy basic operations, functions

• NumPy for Data Analysis

• Importing Pandas

• Pandas overview

• Pandas Series and Data Frames

• Dealing with missing data

• GroupBy, Merging, Concatenating and Joining

• Data Input & Output

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Module 3 – Data Visualization with MatplotLib

• Why visualize data?

• Importing MatplotLib

• Chart: Line Chart, Bar Charts and Pie Charts

• Plotting from Pandas object

• Object Oriented Plotting: Setting axes limits and ticks

• Multiple Plots

• Plot Formatting: Custom Lines, Markers, Labels, Annotations, Colors

Module 4 –Advanced Data Visualization with Seaborn

• Importing Seaborn

• Seaborn overview

• Distribution and Categorical Plotting

• Matrix plots & Grids

• Regression Plots

• Style & Color

• Review Session

UNIT- 2: Statistics for Data Science

Module 5: Introduction to Statistics for Data Science

• Applied statistics in business

• Descriptive Statistics

• Inferential Statistics

• Statistics Terms and definitions

• Types of Data

• Data Measurement Scales

Module 6: Sampling methods in Statistics

• Sampling Data, with and without replacement

• Sampling Methods, Random vs Non-Random

• Measurement on Samples

• Random Sampling methods

• Simple random, Stratified, Cluster, Systematic sampling.

• Biased vs unbiased sampling

• Sampling Error

• Data Collection methods

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Module 7: Exploratory Analysis and Distributions

• Measures of Central Tendencies

• Mean, Median and Mode

• Data Variability: Range, Quartiles, Standard Deviation

• Calculating Standard Deviation

• Z-Score/Standard Score

• Empirical Rule

• Calculating Percentiles

• Outliers

• Distributions Introduction

• Normal Distribution

• Central Limit Theorem

• Histogram - Normalization

• Other Distributions: Poisson, Binomial et.,

• Normality Testing

• Skewness

• Kurtosis

• Measure of Distance

• Euclidean, Manhattan and Murkowski Distance

Module 8: Advanced Statistics for Data Science

• Hypothesis Testing

• Null Hypothesis, P-Value

• Need for Hypothesis Testing in Business

• Two tailed, Left tailed & Right tailed test

• Hypothesis Testing Outcomes: Type I & II errors

• Parametric vs Non-Parametric Testing

• Parametric Tests, T - Tests: One sample, two sample, Paired

• One Way ANOVA

• Importance of Parametric Tests

• Non-Parametric Tests: Chi-Square, Mann-Whitney, Kruskal-Wallis etc.,

• Which Test to Choose?

• Asserting accuracy of Data

• Correlation & Regression

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UNIT-3: Data Science & Applied Machine Learning

Module 9: Machine Learning (ML) Fundamentals

• Machine Learning Introduction

• Applications of Machine Learning

• Machine Learning vs Deep Learning vs Artificial Intelligence

• Languages and platforms

• Machining learning Tools

Module 10: ML Regression and Classification Algorithms

• Linear Regression

• Logistic Regression

• K – Nearest Neighbors

• SVM Classifiers

• K-Means Clustering

• Principle Component Analysis

Module 11: ML Advanced Algorithms & Techniques

• Multiple Linear Regression, Polynomial Regression

• Trees & Forest Classifiers

• XG Boosting

• Model Selection & Cross-Bias Variance

• Natural Language Processing

Module 12: Deep Learning and Neural Networks

• Deep Learning Fundamentals

• Working of Neural Networks

• Gradient Descent and Back Propagation

• TensorFlow & Keras Basics

• Building Artificial Neural Networks (ANN) with Python

• Crafting Advanced ANNs

• Building Convolutional Neural Networks for Image Classification

Module 13: Applications of ML in Bigdata and Could Computing

• Introduction to Tableau and building Dashboards

• Introduction to Hadoop and Spark

• Introduction to ML with Azure or AWS

UNIT-4: Practice Sessions

• Multiple Projects on Machine Learning and Deep Learning

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Mobile: +91 97036 85812

Email: contact@learnxpro.com

Website: www.learnxpro.com

Address: 603, HMDA Maitrivanam, Ameerpet

Hyderabad – 500038, IN

Location: https://goo.gl/maps/mH58NHfm4Bn