data science training chandigarh mohali

Best Data Science Training in chandigarh mohali is provided by our experts with more than 20 years experience. You can meet your trainer, get demo classes before joining the Course. Data Science Training is provided by Manjit Sir having 20 years experience in coding and data analytics. Many students get their training from him. He has placed many students in multi-national companies in data science and data analytics. It is great opportunity for students to join this company. If students are not satisfied then their fee is refundable. We provide 100% Placement in Data Science. We have link with 80+ companies who always hire our students.

Data Science Syllabus

 What is Data science
 Introduction to python data science
 Installation of Pandas,numpy,scipy,sklearn,seaborn,nltk
 Basic terminologies of DS
a. Data science
b. Data scientist
c. Data set
d. Data mining
e. Data visualization
f. Data modeling
g. Data wrangling
h. Big data
i. Machine learning
j. Algorithms
k. Deep learning

Hands on with Pandas – Data Analysis library [Data Processing]

 Why Pandas?
 Features of Pandas
 Data structures in Pandas
a. Series
b. DataFrame
c. Panel
d. Panel4D
 Series creation
a. Using ndarray
b. Using dict
c. Using scalar values
d. Using list
 Accessing elements of Series
a. Using indexing
b. Using slicing
c. Using ranging
d. Using iloc method
e. Using loc method
 Vectorizing operations
a. Vector operations using same index values
b. Vector operations using different index values
 DataFrame creation
a. Using list
b. Using dict
c. Using ndarray
d. Using series
e. Using DataFrame
 Viewing DataFrame elements
a. Using describe function
b. Using column name
c. Using iloc method
d. Using iat method
e. Using head()
f. Using tail()
g. Using index method

Working with Pandas Data
 Handling missing values
a. Using Dropna()
b. Using Fillna()
c. Using add between 2 vector series
 Data operations with customized functions
a. Using groupby()
b. Using sorting
c. Using merge
d. Using duplicate
e. Using concatenation
 Statistical functions in data operations
a. Max()
b. Min()
c. Mean()
d. Std()
 SQL operations in pandas
a. Creating table using sqlite3
b. Executing sql queries
c. Inserting values
d. Fetching records
e. Creating recordset
f. Display resultset
g. Converting resultset into DataFrame
 Data Processing
a. Processing CSV data
b. Processing JSON data
c. Processing XLS data
d. Date and time in data
e. Reading html contents

Numpy – Mathematical Computation
 Why numpy?
 Powerful properties of numpy
 Types of arrays
a. One dimensional
b. Two dimensional
c. Three dimensional
 Attributes of ndarray
a. Using .ndim
b. Using .shape
c. Using .size
d. Using .dtype
 Basic operations
a. ( +, -, *, /, %, //, &, |, ~, <, <=, >, >=, ==, != )
b. Accessing array elements using axis values
c. Indexing with Boolean array
 Creating functions for arrays
a. Using arange()
b. Using linspace()
c. Using ones()
d. Using zeros()
e. Using diag()
f. Using random.rand()
g. Using random.randn()
h. Using random.seed()
 Copy and view
a. Deep copy
b. Shallow copy
c. Simple assignment
 Universal functions
a. Sqrt
b. Cos
c. Floor
d. Exp

 Shape manipulation
a. Using flatten
b. Using reshape
c. Using resize
d. Using split
e. Using stack
 Broadcasting
a. Using tile()
b. Using ones()
c. Using newaxis()

Hands on with Matplotlib library – [Basic Data Visualization]

 Chart properties
a. Creating a chart
b. Labeling the axes
c. Formatting line style and color
d. Saving the chart in a file
 Styling the chart
a. Adding annotations
b. Adding legends
c. Presentation style
 Types of presentation styles
a. Scatter plots
b. Heat maps
c. Bubble chart
d. Bar chart
e. Pie chart
f. XKCD style
g. 3D chart
h. Box and whisker plots
i. Time series plot
j. Graph data / line graph
k. Geographical data
Hands on with Data Distributions (using numpy, pandas, seaborn)

 Why and How Data to be distributed?
a. Calculating mean
b. Calculating median
c. Calculating mode
d. Measuring variance
 Types of distribution
a. Uniform distribution
b. Normal / Gaussian distribution
c. Exponential PDF
d. Binomial distribution PMF
e. Poisson distribution PMF
f. Bernoulli distribution
g. P value
h. Correlation
i. Chi-square test
j. Linear regression

Advanced Data Visualization using SEABORN

 Visualization techniques used
a. Histogram
b. Histogram with grid
c. Distplot
d. Pairplot
e. Scatterplot
f. Lmplot
g. box plot

Data Science series

  • Python Advance Series- Static Methods Indepth Understanding And Implementation In Python
  • Advance House Price Prediction- Exploratory Data Analysis- Part 1
  • Advance House Price Prediction- Exploratory Data Analysis- Part 2
  • Advance House Price Prediction-Feature Engineering Part 1
  • Advance House Price Prediction-Feature Engineering Part 2
  • Advance House Price Prediction-Feature Selection
  • Advance Python Series-Asynchronous Execution(Parallel Execution) With Thread Using Python
  • Vulture Library- How To Find Unused And Dead Code In Python Projects
  • Python Zip Function- Easy Parallel Iteration for Multiple Iterators
  • Pdf Password Protection Using Python
  • PIP Freeze- Creating Packages(Requirements.txt) For The Application
  • Logging Implementation In Python
  • Secure Hash Algorithms Using Python- SHA256,SHA384,SHA224,SHA512,SHA1- Hashing In BlockChain
  • Numba Library- Let’s Make Python Faster

Data Science Libraries and tools

  • Pandas
  • Numpy
  • matplotlib
  • scikit learn
  • Keras
  • seaborn
  • appache Spark
  • Tableu
  • PowerBI
  • OpenCV
  • nltk
  • Neural Network
  • Regressions
  • Tensorflow
  • Pytorch
  • mongodb
  • statistics

Data Science Training with 100%placement

Data Science course includes various libraries and tools to analyze huge data. Microsoft Excel and Power BI one of the best tools to analyze data. We provide training in various tools and libraries of data science course in chandigarh and Mohali, We provide 100%placement in data science. We have placed many students in Multinational companies in india and abroad.