With the widespread use of IoT, there is growing interest in improving the efficiency of a system by analyzing data collected from various sources. At the same time, various services exploiting time-series data such as the density of fine particles, weathers, and stock prices emerge as a new competitive service. In this course, we study deep learning for analyzing time series data with TensorFlow. To exploit the correlation structure in time series, two basic neural networks with memory, RNN (recurrent neural network)와 LSTM (long short term memory) will be studied. Then more advanced NN such as bidirectional LSTM, attentional LSTM, and convolutional LSTM will be also learned. This course will be a mix of theory and practice while the practice will proceed with stock data mainly.